1. Libraries and functions

1.1 Libraries

Load the required libraries.

library(tidyverse)
library(sf)
library(here)
library(readxl)
library(scales)
library(DT)
library(brms)
library(tidybayes)
library(patchwork)
library(marginaleffects)
library(ggrepel)
library(scico)
library(ggdensity)
library(ggpubr)
library(units)
library(glue)
library(ggh4x)

1.2 Helper functions

Functions that we will use throughout the script

#labeller for years
year_labels <- c(1950:1963)

#The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
#Segment for graphs to match ACF period
acf_start <- decimal_date(ymd("1957-03-11"))
acf_end <- decimal_date(ymd("1957-04-12"))

Function for counterfactual plots



plot_counterfactual <- function(model_data, model, population_denominator, outcome, grouping_var=NULL, re_formula,...){
  
  #labeller for years
  year_labels <- c(1950:1964) #extra year for the extant of the x-axis

  #The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
  #Segment for graphs to match ACF period
  acf_start <- decimal_date(ymd("1957-03-11"))
  acf_end <- decimal_date(ymd("1957-04-12"))

  summary <- {{model_data}} %>%
    select(year, year2, y_num, acf_period, {{population_denominator}}, {{outcome}}, {{grouping_var}}) %>%
    add_epred_draws({{model}}, re_formula={{re_formula}}) %>%
    group_by(year2, acf_period, {{grouping_var}}) %>%
    mean_qi() %>%
    mutate(.epred_inc = .epred/{{population_denominator}}*100000,
          .epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
          .epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
    mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                  acf_period=="c. post-acf" ~ "Post Intervention"))



  #create the counterfactual (no intervention), and summarise
  
  counterfact <-
    add_epred_draws(object = {{model}},
                    newdata = {{model_data}} %>%
                                  select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, {{outcome}}) %>%
                                  mutate(acf_period = "a. pre-acf"), re_formula={{re_formula}}) %>%
    group_by(year2, acf_period, {{grouping_var}}) %>%
    mean_qi() %>%
    mutate(.epred_inc = .epred/{{population_denominator}}*100000,
         .epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
         .epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
    mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))
  


  #plot the intervention effect
p <- summary %>%
    droplevels() %>%
    ggplot() +
    geom_vline(aes(xintercept=acf_start, linetype="Mass CXR screening intervention")) +
    geom_vline(aes(xintercept=acf_end, linetype="Mass CXR screening intervention")) +
    geom_ribbon(aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
    geom_ribbon(data = counterfact %>% filter(year>=1956), 
                aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
    geom_line(data = counterfact %>% filter(year>=1956), 
              aes(y=.epred_inc, x=year2, colour="Counterfactual")) +
    geom_line(aes(y=.epred_inc, x=year2, group=acf_period,  colour=acf_period)) +
    geom_point(data = {{model_data}} %>%
                 mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Pre-ACF",
                                               acf_period=="b. acf" ~ "ACF",
                                               acf_period=="c. post-acf" ~ "Post-ACF")), 
               aes(y={{outcome}}, x=year2, shape=fct_relevel(acf_period,
                                                             "Pre-ACF",
                                                             "ACF",
                                                             "Post-ACF")), size=2) +
    theme_grey() +
    scale_y_continuous(labels=comma, limits =c(0,400)) +
    scale_x_continuous(labels = year_labels,
                       breaks = year_labels) +
    scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:", na.translate = F) +
    scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:", na.translate = F) +
    scale_shape_discrete(name="Empirical data (period):", na.translate = F) +
    scale_linetype_manual(values = 2, name="") +
    labs(
      x = "",
      y = "CNR (per 100,000)"
    ) +
    guides(x = "axis_truncated", y = "axis_truncated") +
    theme(legend.position = "bottom",
          legend.box="vertical", 
          text = element_text(size=10),
          axis.text.x = element_text(size=10, angle = 90, hjust=1, vjust=0.5),
          legend.text = element_text(size=10),
          legend.spacing.y = unit(0.1, 'cm'),
          axis.line = element_line(colour = "black")) 

    facet_vars <- vars(...)

  if (length(facet_vars) != 0) {
    p <- p + facet_wrap(facet_vars)
  }
  p

}

Function for calculating measures of change over time (RR.peak, RR.level, RR.slope)


summarise_change <- function(model_data, model, population_denominator, grouping_var = NULL, re_formula = NULL) {
  
  #functions for calculating RR.peak
  #i.e. relative case notification rate in 1957 vs. counterfactual trend for 1957
  
  grouping_var <- enquo(grouping_var)
  
  if (!is.null({{grouping_var}})) {
    
    #make the prediction matrix, conditional on whether we want random effects included or not.
    out <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num == 8),
                    acf_period = c("a. pre-acf", "b. acf")
    )
  } else {
    
    out <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num == 8),
                    acf_period = c("a. pre-acf", "b. acf")
    )
  }
  
  peak_draws <- add_epred_draws(newdata = out,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
    mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
    group_by(.draw, !!grouping_var) %>%
    summarise(estimate = last(epred_cnr)/first(epred_cnr)) %>%
    ungroup() %>%
    mutate(measure = "RR.peak")
  
  peak_summary <- peak_draws %>%
    group_by(!!grouping_var) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.peak")
  
  
  #functions for calculating RR.level
  #i.e. relative case notification rate in 1958 vs. counterfactual trend for 1958
  
    if (!is.null({{grouping_var}})) {
    out2 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num == 9),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  } else {
    
    out2 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num == 9),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  }
  
    level_draws <- add_epred_draws(newdata = out2,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
    arrange(y_num, .draw) %>%
    mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
    group_by(.draw, !!grouping_var) %>%
    summarise(estimate = last(epred_cnr)/first(epred_cnr)) %>%
    ungroup() %>%
    mutate(measure = "RR.level")
  
  level_summary <- level_draws %>%
    group_by(!!grouping_var) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.level")
    
    
  #functions for calculating RR.slope
  #i.e. relative change in case notification rate in 1958-1963 vs. counterfactual trend for 1959-1963
  
    if (!is.null({{grouping_var}})) {
    out3 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  } else {
    
    out3 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  }
  
    slope_draws <- add_epred_draws(newdata = out3,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
        arrange(y_num) %>%
        ungroup() %>%
        mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
        group_by(.draw, y_num, !!grouping_var) %>%
        summarise(slope = last(epred_cnr)/first(epred_cnr)) %>%
        ungroup() %>%
        group_by(.draw, !!grouping_var) %>%
        summarise(estimate = last(slope)/first(slope)) %>%
        mutate(measure = "RR.slope")
  
  slope_summary <- slope_draws %>%
     group_by(!!grouping_var) %>%
      mean_qi(estimate) %>%
      mutate(measure = "RR.slope")
    
  #gather all the results into a named list
    lst(peak_draws=peak_draws, peak_summary=peak_summary, 
        level_draws=level_draws, level_summary=level_summary, 
        slope_draws=slope_draws, slope_summary=slope_summary)
  
}

Function for calculating difference from counterfactual


calculate_counterfactual <- function(model_data, model, population_denominator, grouping_var=NULL, re_formula=NA){
  
  #effect vs. counterfactual
  counterfact <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}) %>%
                                    mutate(acf_period = "a. pre-acf"),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, year, {{grouping_var}}, acf_period) %>%
      mutate(.epred_inc_counterf = .epred/{{population_denominator}}*100000, .epred_counterf=.epred)  %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, {{population_denominator}}, .draw, .epred_counterf, .epred_inc_counterf, {{grouping_var}})
  
  #Calcuate case notification rate per draw, then summarise.
  post_change <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, acf_period),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, year, {{grouping_var}}, acf_period) %>%
      mutate(.epred_inc = .epred/{{population_denominator}}*100000)  %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, {{population_denominator}}, {{grouping_var}}, .draw, .epred, .epred_inc, {{grouping_var}}) 
  
  #for the overall period
    counterfact_overall <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}) %>%
                                    mutate(acf_period = "a. pre-acf"),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, {{grouping_var}}) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select({{population_denominator}}, .draw, .epred, {{grouping_var}})  %>%
      group_by(.draw, {{grouping_var}}) %>%
      summarise(.epred_counterf = sum(.epred)) 
  
  #Calcuate case notification rate per draw, then summarise.
  post_change_overall <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, acf_period),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, {{grouping_var}}) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select({{population_denominator}}, {{grouping_var}}, .draw, .epred) %>%
      group_by(.draw, {{grouping_var}}) %>%
      summarise(.epred = sum(.epred)) 
  
  
counter_post <-
  left_join(counterfact, post_change) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf,
           diff_inc100k = .epred_inc - .epred_inc_counterf,
           rr_inc100k = .epred_inc/.epred_inc_counterf) %>%
    group_by(year, {{grouping_var}}) %>%
    mean_qi(cases_averted, pct_change, diff_inc100k, rr_inc100k) %>%
    ungroup()

counter_post_overall <-
  left_join(counterfact_overall, post_change_overall) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf) %>%
    group_by({{grouping_var}}) %>%
    mean_qi(cases_averted, pct_change) %>%
    ungroup()

lst(counter_post, counter_post_overall)

}

Function for tidying up counterfactuals (mostly for making nice tables)


tidy_counterfactuals <- function(data){
  data %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(year = as.character(year),
            cases_averted = glue::glue("{cases_averted} ({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change} ({pct_change.lower} to {pct_change.upper})"),
            diff_inc = glue::glue("{diff_inc100k} ({diff_inc100k.lower} to {diff_inc100k.upper})"),
            rr_inc = glue::glue("{rr_inc100k} ({rr_inc100k.lower} to {rr_inc100k.upper})"))
}


tidy_counterfactuals_overall <- function(data){
  data %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(year = as.character(year),
            cases_averted = glue::glue("{cases_averted} ({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change} ({pct_change.lower} to {pct_change.upper})"))
}

2. Data

Import datasets for analysis

2.1 Shapefiles

Make a map of Glasgow wards


glasgow_wards_1951 <- st_read(here("mapping/glasgow_wards_1951.geojson"))
Reading layer `glasgow_wards_1951' from data source 
  `/Users/petermacpherson/Dropbox/Projects/Historical TB ACF 2023-11-28/Work/analysis/glasgow-cxr/mapping/glasgow_wards_1951.geojson' 
  using driver `GeoJSON'
Simple feature collection with 37 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -4.393502 ymin: 55.77464 xmax: -4.070411 ymax: 55.92814
Geodetic CRS:  WGS 84

#read in Scotland boundary
scotland <- st_read(here("mapping/Scotland_boundary/Scotland boundary.shp"))
Reading layer `Scotland boundary' from data source 
  `/Users/petermacpherson/Dropbox/Projects/Historical TB ACF 2023-11-28/Work/analysis/glasgow-cxr/mapping/Scotland_boundary/Scotland boundary.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 1 field
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 5513 ymin: 530249 xmax: 470332 ymax: 1220302
Projected CRS: OSGB36 / British National Grid
#make a bounding box for Glasgow
bbox <- st_bbox(glasgow_wards_1951) |> st_as_sfc()

#plot scotland with a bounding box around the City of Glasgow
scotland_with_bbox <- ggplot() +
  geom_sf(data = scotland, fill="antiquewhite") +
  geom_sf(data = bbox, colour = "#C60C30", fill="antiquewhite") +
  theme_void() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA, linewidth = 0.5),
        panel.background = element_rect(fill = "#EAF7FA", size = 0.3))

#plot the wards
#note we tidy up some names to fit on map
glasgow_ward_map <- glasgow_wards_1951 %>%
  mutate(ward = case_when(ward=="Shettleston and Tollcross" ~ "Shettleston and\nTollcross",
                          ward=="Partick (West)" ~ "Partick\n(West)",
                          ward=="Partick (East)" ~ "Partick\n(East)",
                          ward=="North Kelvin" ~ "North\nKelvin",
                          ward=="Kinning Park" ~ "Kinning\nPark",
                          TRUE ~ ward)) %>%
  
  ggplot() +
  geom_sf(aes(fill=division)) +
  geom_sf_label(aes(label = ward), size=3, fill=NA, label.size = NA, colour="black") +
  #scale_colour_identity() +
  scale_fill_brewer(palette = "Set3", name="City of Glasgow Division") +
  theme_grey() +
  labs(x="",
       y="",
       fill="Division") +
  theme(legend.position = "top",
        
        panel.border = element_rect(colour = "grey78", fill=NA, linewidth = 0.5),
        panel.background = element_rect(fill = "antiquewhite", size = 0.3),
        panel.grid.major = element_line(color = "grey78")) +
  guides(fill=guide_legend(title.position = "top", title.hjust = 0.5, title.theme = element_text(face="bold")))

#add the map of scotland as an inset
glasgow_ward_map + inset_element(scotland_with_bbox, 0.75, 0, 1, 0.4)

ggsave(here("figures/s1.png"), height=10, width = 12)

NA
NA

Calculate areas per geographical unit

sf_use_s2(FALSE) #https://github.com/r-spatial/sf/issues/1762

glasgow_wards_1951 <- glasgow_wards_1951 %>%
  mutate(area = st_area(glasgow_wards_1951))


glasgow_wards_1951$area_km <- units::set_units(glasgow_wards_1951$area, km^2)

Make division shape files, and calculate area (stopped working, need to fix!)


# glasgow_divisions_1951 <- glasgow_wards_1951 %>%
#   group_by(division) %>% 
#   summarize(geometry = st_union(geometry)) %>%
#   nngeo::st_remove_holes() %>%
#   mutate(area = st_area(glasgow_divisions_1951))
# 
# glasgow_divisions_1951$area_km <- units::set_units(glasgow_divisions_1951$area, km^2)

3. Denominators

Load in the datasets for denonomiators, and check for consistency.


overall_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "overall_population")

overall_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
overall_pops <- overall_pops %>%
  mutate(year2 = year+0.5)

Note, we have three population estimates:

  1. Population without institutionalised people or people in shipping
  2. Population in institutions
  3. Population in shipping

(Population in shipping is estimated from the 1951 census, so is the same for most years)

3.1 Overall population

First, plot the total population


overall_pops %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2), alpha=0.5, colour = "mediumseagreen", fill="mediumseagreen") +
  geom_point(aes(y=total_population, x=year2), colour = "mediumseagreen") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(
    title = "Glasgow Corporation: total population",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist()

NA
NA

Now the population excluding institutionalised and shipping population


overall_pops %>%
  ggplot() +
  geom_area(aes(y=population_without_inst_ship, x=year2), alpha=0.5, colour = "purple", fill="purple") +
  geom_point(aes(y=population_without_inst_ship, x=year2), colour = "purple") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(
    title = "Glasgow Corporation: population excluding institutionalised and shipping",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist()

NA
NA

3.2 Population by Ward

There are 5 Divisions containing 37 Wards in the Glasgow Corporation, with consistent boundaries over time.

#look-up table for divisions and wards
ward_lookup <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "divisions_wards")


ward_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "ward_population")

ward_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
ward_pops <- ward_pops %>%
  mutate(year2 = year+0.5)

#Get the Division population
division_pops <- ward_pops %>%
  group_by(division, year) %>%
  summarise(population_without_inst_ship = sum(population_without_inst_ship, na.rm = TRUE),
            institutions = sum(institutions, na.rm = TRUE),
            shipping = sum(shipping, na.rm = TRUE),
            total_population = sum(total_population, na.rm = TRUE))
`summarise()` has grouped output by 'division'. You can override using the `.groups` argument.
division_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()
NA

Plot the overall population by Division and Ward


division_pops %>%
  mutate(year2 = year+0.5) %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2, colour=division, fill=division), alpha=0.8) +
  geom_point(aes(y=total_population, x=year2, colour=division)) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  facet_wrap(division~.) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_brewer(palette = "Set3", name = "") +
  scale_colour_brewer(palette = "Set3", name = "") +
  labs(
    title = "Glasgow Corporation: total population by Division",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

NA
NA

ward_pops %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2, colour=division, fill=division)) +
  geom_point(aes(y=total_population, x=year2, colour=division), colour="black") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  facet_wrap(ward~., ncol=6) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_brewer(palette = "Set3", name="Division") +
  scale_colour_brewer(palette = "Set3", name = "Division") +
  labs(
    x = "",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

ggsave(here("figures/s2.png"), height=14, width=12)

Approximately, how many person-years of follow-up do we have?


overall_pops %>%
  ungroup() %>%
  summarise(across(year, length, .names = "years"),
            across(c(population_without_inst_ship, total_population), sum)) %>%
  mutate(across(where(is.double), comma)) %>%
  datatable()
NA
NA

Change in population by ward


ward_pops %>%
  group_by(ward) %>%
  summarise(pct_change_pop = (last(population_without_inst_ship) - first(population_without_inst_ship))/first(population_without_inst_ship)) %>%
  mutate(pct_change_pop = percent(pct_change_pop)) %>%
  arrange(pct_change_pop) %>%
  datatable()
NA
NA
NA

Output population density by ward and divison for regression modelling

Wards first

(stopped working, need to fix)


# ward_covariates <-  glasgow_wards_1951 %>%
#   left_join(ward_pops) %>%
#   mutate(people_per_km_sq = as.double(population_without_inst_ship/area_km))
# 
# #plot it out
# 
# ward_covariates %>%
#   ggplot() +
#   geom_sf(aes(fill=people_per_km_sq)) + 
#   facet_wrap(year~., ncol=7) +
#   scale_fill_viridis_c(option="A") +
#   theme(legend.position = "bottom",
#         axis.text.x = element_text(angle = 45, hjust=1))
# 
# ggsave(here("figures/ward_pop_density.png"), width=10)
# 
# write_rds(ward_covariates, here("populations/ward_covariates.rds"))

Now divisions first

(stopped working, need to fix)


# division_covariates <-  glasgow_divisions_1951 %>%
#   left_join(division_pops) %>%
#   mutate(people_per_km_sq = as.double(total_population/area_km))
# 
# #plot it out
# 
# division_covariates %>%
#   ggplot() +
#   geom_sf(aes(fill=people_per_km_sq)) + 
#   geom_sf_label(aes(label = division), size=3, fill=NA, label.size = NA, colour="black", family = "Segoe UI") +
#   facet_wrap(year~., ncol=7) +
#   scale_fill_viridis_c(option="G") +
#   theme(legend.position = "bottom",
#         axis.text.x = element_text(angle = 45, hjust=1))
# 
# ggsave(here("figures/division_pop_density.png"), width=10)
# 
# write_rds(division_covariates, here("populations/division_covariates.rds"))

3.3 Population by age and sex


age_sex <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "age_sex_population") %>%
  pivot_longer(cols = c(male, female),
               names_to = "sex")

#collapse down to smaller age groups to be manageable
age_sex <- age_sex %>%
  ungroup() %>%
  mutate(age = case_when(age == "0 to 4" ~ "00 to 04",
                         age == "5 to 9" ~ "05 to 14",
                         age == "10 to 14" ~ "05 to 14",
                         age == "15 to 19" ~ "15 to 24",
                         age == "20 to 24" ~ "15 to 24",
                         age == "25 to 29" ~ "25 to 34",
                         age == "30 to 34" ~ "25 to 34",
                         age == "35 to 39" ~ "35 to 44",
                         age == "40 to 44" ~ "35 to 44",
                         age == "45 to 49" ~ "45 to 59",
                         age == "50 to 54" ~ "45 to 59",
                         age == "55 to 59" ~ "45 to 59",
                         TRUE ~ "60 & up")) %>%
  group_by(year, age, sex) %>%
  mutate(value = sum(value)) %>%
  ungroup()



m_age_sex <- lm(value ~ splines::ns(year, knots = 3)*age*sex, data = age_sex)

summary(m_age_sex)
Warning: essentially perfect fit: summary may be unreliable

Call:
lm(formula = value ~ splines::ns(year, knots = 3) * age * sex, 
    data = age_sex)

Residuals:
       Min         1Q     Median         3Q        Max 
-1.185e-10  0.000e+00  0.000e+00  0.000e+00  1.185e-10 

Coefficients: (14 not defined because of singularities)
                                                    Estimate Std. Error    t value Pr(>|t|)    
(Intercept)                                        5.222e+04  2.040e-10  2.559e+14   <2e-16 ***
splines::ns(year, knots = 3)1                     -8.043e+03  4.071e-10 -1.976e+13   <2e-16 ***
splines::ns(year, knots = 3)2                             NA         NA         NA       NA    
age05 to 14                                        3.669e+04  2.499e-10  1.468e+14   <2e-16 ***
age15 to 24                                       -3.893e+03  2.499e-10 -1.558e+13   <2e-16 ***
age25 to 34                                       -3.996e+04  2.499e-10 -1.599e+14   <2e-16 ***
age35 to 44                                       -4.230e+04  2.499e-10 -1.693e+14   <2e-16 ***
age45 to 59                                        5.459e+04  2.356e-10  2.317e+14   <2e-16 ***
age60 & up                                         7.533e+04  2.204e-10  3.418e+14   <2e-16 ***
sexmale                                            3.374e+03  2.886e-10  1.169e+13   <2e-16 ***
splines::ns(year, knots = 3)1:age05 to 14         -1.863e+03  4.985e-10 -3.737e+12   <2e-16 ***
splines::ns(year, knots = 3)2:age05 to 14                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age15 to 24          7.533e+04  4.985e-10  1.511e+14   <2e-16 ***
splines::ns(year, knots = 3)2:age15 to 24                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age25 to 34          1.325e+05  4.985e-10  2.658e+14   <2e-16 ***
splines::ns(year, knots = 3)2:age25 to 34                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age35 to 44          1.380e+05  4.985e-10  2.769e+14   <2e-16 ***
splines::ns(year, knots = 3)2:age35 to 44                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age45 to 59          3.474e+03  4.700e-10  7.390e+12   <2e-16 ***
splines::ns(year, knots = 3)2:age45 to 59                 NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age60 & up          -8.453e+04  4.397e-10 -1.923e+14   <2e-16 ***
splines::ns(year, knots = 3)2:age60 & up                  NA         NA         NA       NA    
splines::ns(year, knots = 3)1:sexmale             -1.994e+03  5.757e-10 -3.464e+12   <2e-16 ***
splines::ns(year, knots = 3)2:sexmale                     NA         NA         NA       NA    
age05 to 14:sexmale                                1.053e+04  3.534e-10  2.980e+13   <2e-16 ***
age15 to 24:sexmale                                2.352e+04  3.534e-10  6.656e+13   <2e-16 ***
age25 to 34:sexmale                                1.355e+04  3.534e-10  3.833e+13   <2e-16 ***
age35 to 44:sexmale                               -1.727e+03  3.534e-10 -4.888e+12   <2e-16 ***
age45 to 59:sexmale                                2.774e+03  3.332e-10  8.324e+12   <2e-16 ***
age60 & up:sexmale                                -7.761e+04  3.117e-10 -2.490e+14   <2e-16 ***
splines::ns(year, knots = 3)1:age05 to 14:sexmale -2.049e+04  7.051e-10 -2.906e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age05 to 14:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age15 to 24:sexmale -6.780e+04  7.051e-10 -9.616e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age15 to 24:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age25 to 34:sexmale -3.804e+04  7.051e-10 -5.396e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age25 to 34:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age35 to 44:sexmale -1.171e+04  7.051e-10 -1.661e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age35 to 44:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age45 to 59:sexmale -3.473e+04  6.647e-10 -5.224e+13   <2e-16 ***
splines::ns(year, knots = 3)2:age45 to 59:sexmale         NA         NA         NA       NA    
splines::ns(year, knots = 3)1:age60 & up:sexmale   1.056e+05  6.218e-10  1.698e+14   <2e-16 ***
splines::ns(year, knots = 3)2:age60 & up:sexmale          NA         NA         NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.074e-11 on 44 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 6.006e+29 on 27 and 44 DF,  p-value: < 2.2e-16
age_levels <- age_sex %>% select(age) %>% distinct() %>% pull() 

age_sex_nd <- 
  crossing(
    age=age_levels,
    sex=c("male", "female"),
    year = 1950:1963
  )

pred_pops <- age_sex_nd %>% modelr::add_predictions(m_age_sex)
Warning: prediction from a rank-deficient fit may be misleading
pred_pops %>%
  ggplot(aes(x=year, y=pred, colour=age)) +
  geom_line() +
  geom_point() +
  facet_grid(sex~.) +
  scale_y_continuous(labels = comma, limits = c(0, 125000))


#How well do they match up with our overall populations?
pred_pops %>%
  group_by(year) %>%
  summarise(sum_pred_pop = sum(pred)) %>%
  right_join(overall_pops) %>%
  select(year, sum_pred_pop, population_without_inst_ship, total_population) %>%
  pivot_longer(cols = c(sum_pred_pop, population_without_inst_ship, total_population)) %>%
  ggplot(aes(x=year, y=value, colour=name)) +
  geom_point() +
  scale_y_continuous(labels = comma, limits = c(800000, 1250000))
Joining with `by = join_by(year)`

pred_pops %>%
  group_by(year, sex) %>%
  summarise(sum = sum(pred)) %>%
  group_by(year) %>%
  mutate(sex_ratio = first(sum)/last(sum))
`summarise()` has grouped output by 'year'. You can override using the `.groups` argument.

Population pyramids


label_abs <- function(x) {
  comma(abs(x))
}


pred_pops %>%
  ungroup() %>%
  group_by(year) %>%
  mutate(year_pop = sum(pred),
         age_sex_pct = percent(pred/year_pop, accuracy=0.1)) %>%
  mutate(sex = case_when(sex=="male" ~ "Male",
                         sex=="female" ~ "Female")) %>%
  ggplot(
    aes(x = age, fill = sex, 
        y = ifelse(test = sex == "Female",yes = -pred, no = pred))) + 
  geom_bar(stat = "identity") +
  geom_text(aes(label = age_sex_pct),
            position= position_stack(vjust=0.5), colour="black", size=2.5) +
  facet_wrap(year~., ncol=7) +
  coord_flip() +
  scale_y_continuous(labels = label_abs) +
  scale_fill_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  theme_ggdist() +
  theme(axis.text.x = element_text(angle=90, hjust = 1, vjust=0.5),
        legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="", y="") 


ggsave(here("figures/s3.png"), width=10)
Saving 10 x 4.5 in image

Not perfect, but resonably good. But ahhhhh… the age groups don’t align with the case notification age groups! Come back to think about this later.

4. Tuberculosis cases

Import the tuberculosis cases dataset

4.1 Overall notifications

Overall, by year.


cases_by_year <- read_xlsx("2023-11-28_glasgow-acf.xlsx", sheet = "by_year")

cases_by_year%>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()


#shift year to midpoint
cases_by_year <- cases_by_year %>%
  mutate(year2 = year+0.5)

Plot the overall number of case notified per year, by pulmonary and extra pulmonary classification.


cases_by_year %>%
  select(-total_notifications, -year) %>%
  pivot_longer(cols = c(pulmonary_notifications, `non-pulmonary_notifications`)) %>%
  mutate(name = case_when(name == "pulmonary_notifications" ~ "Pulmonary TB",
                          name == "non-pulmonary_notifications" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

NA
NA

4.2 Notifications by Division

Read in the datasets and merge together.


#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")

#get the ward sheets
ward_sheets <- enframe(all_sheets) %>%
  filter(grepl("by_ward", value)) %>%
  pull(value)


cases_by_ward_sex_year <- map_df(ward_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

cases_by_ward_sex_year %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()
NA

Aggregate together to get cases by division


cases_by_division <- cases_by_ward_sex_year %>%
  left_join(ward_lookup) %>%
  group_by(division, year, tb_type) %>%
  summarise(cases = sum(cases, na.rm = TRUE))
Joining with `by = join_by(ward)``summarise()` has grouped output by 'division', 'year'. You can override using the `.groups` argument.
#shift year to midpoint
cases_by_division <- cases_by_division %>%
  mutate(year2 = year+0.5) %>%
  ungroup()

cases_by_division  %>%
  select(-year2) %>%
  select(year, everything()) %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()


cases_by_division %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(division~., scales = "free_y") +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications by Division",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

4.3 Notifications by ward



cases_by_ward <- cases_by_ward_sex_year %>%
  group_by(ward, year, tb_type) %>%
  summarise(cases = sum(cases, na.rm = TRUE)) %>%
  ungroup()
`summarise()` has grouped output by 'ward', 'year'. You can override using the `.groups` argument.
cases_by_ward %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  select(year, everything()) %>%
  datatable()

#shift year to midpoint
cases_by_ward <- cases_by_ward %>%
  mutate(year2 = year+0.5)

cases_by_ward %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.8) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(ward~., scales = "free_y") +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications by Ward",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme(legend.position = "bottom")

NA
NA

4.4 Notifications by age and sex

As we don’t have denominators, we will just model the change in counts.


#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")

#get the ward sheets
age_sex_sheets <- enframe(all_sheets) %>%
  filter(grepl("by_age_sex", value)) %>%
  pull(value)


cases_by_age_sex <- map_df(age_sex_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

cases_by_age_sex %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()
NA
NA

4.5 Uptake of screening

What percentage of adults (15+ participated in the intervention in 1957)?

# 
# pred_pops %>%
#   ungroup() %>%
#   filter(year==1957) %>%
#   filter(age != "00 to 04",
#          age != "05 to 14") %>%
#   summarise(total_pop = sum(pred)) %>%
#   mutate(cxr_screened = 622349) %>%
#   mutate(pct_pop_cxr_screened = percent(cxr_screened/total_pop))
# 
# pred_pops %>%
#   ungroup() %>%
#   filter(year==1957) %>%
#   filter(age != "00 to 04",
#          age != "05 to 14") %>%
#   summarise(total_pop = sum(pred), .by=sex) %>%
#   mutate(cxr_screened = c(340474, 281875)) %>%
#   mutate(pct_pop_cxr_screened = percent(cxr_screened/total_pop))

Note that in the Report of Sir Kenneth Cowan, we have the following estimates of participation (we will use these for the manuscript, as they are not based on my estimates)

male_adult_resident_participation <- 281875
female_adult_resident_participation <- 340474
male_adult_resident_population <- 381713
female_adult_resident_population <- 437588

#overall participation
(male_adult_resident_participation+female_adult_resident_participation)/(male_adult_resident_population+female_adult_resident_population)
[1] 0.7596097
#male participation
male_adult_resident_participation/male_adult_resident_population
[1] 0.7384475
#female participation
female_adult_resident_participation/female_adult_resident_population
[1] 0.7780698

Look at uptake of screening by age and sex



uptake_age_sex <- read_xlsx("2024-03-26_mass_xray_uptake.xlsx", sheet = "uptake_age_sex")

uptake_age_sex %>%
  mutate(uptake = examined/resident_population) %>%
  mutate(examined_l = comma(examined),
         resident_population_l = comma(resident_population),
         uptake_l = percent(uptake, accuracy=0.1)) %>%
  mutate(label = glue("{examined_l}/{resident_population_l} ({uptake_l})")) %>%
  filter(age !="00-14") %>%
  mutate(sex = case_when(sex=="m" ~ "Male",
                         sex=="f" ~ "Female")) %>%
  ggplot(aes(x=age, y=uptake, group=sex, fill=sex)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_text(aes(label=uptake_l), position = position_dodge(width=0.85),vjust=2) +
  scale_y_continuous(labels=percent) +
  scale_fill_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="", y="")

ggsave(here("figures/s4.png"))
Saving 7.29 x 4.5 in image

Uptake by division


uptake_division <- read_xlsx("2024-03-26_mass_xray_uptake.xlsx", sheet = "uptake_division")

division_pops %>%
  filter(year==1957) %>%
  select(division, population_without_inst_ship) %>%
  left_join(uptake_division) %>%
  mutate(pct_pop_examined = examined/population_without_inst_ship)
Joining with `by = join_by(division)`

5 TB case notification rates

5.1 Overall TB case notification rates

Now calculate case notification rates per 100,000 population

Merge the notification and population denominator datasets together.

Here we need to include the whole population (with shipping and institutions) as they are included in the notifications.


overall_inc <- overall_pops %>%
  left_join(cases_by_year)
Joining with `by = join_by(year, year2)`
overall_inc <- overall_inc %>%
  mutate(inc_pulm_100k = pulmonary_notifications/total_population*100000,
         inc_ep_100k = `non-pulmonary_notifications`/total_population*100000,
         inc_100k = total_notifications/total_population*100000)

overall_inc %>%
  select(year, inc_100k, inc_pulm_100k, inc_ep_100k) %>%
  mutate_at(.vars = vars(inc_100k, inc_pulm_100k, inc_ep_100k),
            .funs = funs(round)) %>%
  datatable()
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

overall_inc %>%
  select(year2, inc_pulm_100k, inc_ep_100k) %>%
  pivot_longer(cols = c(inc_pulm_100k, `inc_ep_100k`)) %>%
  mutate(name = case_when(name == "inc_pulm_100k" ~ "Pulmonary TB",
                          name == "inc_ep_100k" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

NA
NA
NA

Change in case notification rates pre-intervention

#pre-ACF
overall_inc %>%
  filter(year %in% 1950:1956) %>%
  summarise(change = (((last(inc_pulm_100k)-first(inc_pulm_100k))/first(inc_pulm_100k))/7)*100)

#post-ACF
overall_inc %>%
  filter(year %in% 1958:1963) %>%
  summarise(change = (((last(inc_pulm_100k)-first(inc_pulm_100k))/first(inc_pulm_100k))/6)*100)
NA

5.2 TB case notification rates by Division


division_inc <- division_pops %>%
  left_join(cases_by_division)
Joining with `by = join_by(division, year)`
division_inc <- division_inc %>%
  mutate(inc_100k = cases/total_population*100000)

division_inc %>%
  select(year, division, tb_type, inc_100k) %>%
  mutate_at(.vars = vars(inc_100k),
            .funs = funs(round)) %>%
  datatable()
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

division_inc %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(division~.) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate, by Division",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

NA
NA
NA

5.2 TB case notification rates by Ward

Here we will filter out the institutions and harbour from the denominators, as we don’t have reliable population denominators for them.


ward_inc <- ward_pops %>%
  left_join(cases_by_ward)
Joining with `by = join_by(ward, year, year2)`
ward_inc <- ward_inc %>%
  mutate(inc_100k = cases/population_without_inst_ship*100000)

ward_inc %>%
  select(year, ward, tb_type, inc_100k) %>%
  mutate_at(.vars = vars(inc_100k),
            .funs = funs(round)) %>%
  datatable()
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))

ward_inc %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(ward~.) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate, by Ward",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Incidence (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme(legend.position = "bottom")

NA
NA
NA
NA

On a map


st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
  filter(tb_type=="Pulmonary") %>%
  ggplot() +
  geom_sf(aes(fill=inc_100k)) +
  facet_wrap(year~., ncol = 7) +
  scale_fill_viridis_c(name="Case notification rate (per 100,000)",
                       option = "A") +
  theme_ggdist() +
  theme(legend.position = "top",
        legend.key.width = unit(2, "cm"),
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  guides(fill=guide_colorbar(title.position = "top"))
Joining with `by = join_by(division, ward, ward_number)`

6. TB Mortality

6.1 Overall Mortality

Import the TB mortality data.

First, overall deaths. Note that in the original reports, we have a pulmonary TB death rate per million for all years, and numbers of pulmonary TB deaths for each year apart from 1950.


#get the overall mortality sheets
deaths_sheets <- enframe(all_sheets) %>%
  filter(grepl("deaths", value)) %>%
  pull(value)


overall_deaths <- map_df(deaths_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

overall_deaths %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()
NA
NA
NA

Plot the raw numbers of pulmonary deaths


overall_deaths %>%
  ggplot(aes(x=year, y=pulmonary_deaths)) +
  geom_line(colour = "#DE0D92") +
  geom_point(colour = "#DE0D92") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  labs(y="Pulmonary TB deaths per year",
       x = "Year",
       title = "Numbers of pulmonary TB deaths",
       subtitle = "Glasgow, 1950-1963",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: no data for 1950") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))

NA
NA

Now the incidence of pulmonary TB death

overall_deaths %>%
  ggplot(aes(x=year, y=pulmonary_death_rate_per_100k)) +
  geom_line(colour = "#4D6CFA") +
  geom_point(colour = "#4D6CFA") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(y="Annual incidence of death (per 100,000)",
       x = "Year",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))

ggsave(here("figures/s8.png"), width=10)
Saving 10 x 4.5 in image

6. Table 1

Make Table 1 here, and save for publication.


overall_pops %>% 
  select(year, total_population) %>%
  left_join(overall_inc %>%
              select(year, 
                     pulmonary_notifications, inc_pulm_100k,
                     `non-pulmonary_notifications`, inc_ep_100k,
                     total_notifications, inc_100k)) %>%
  left_join(overall_deaths %>%
              select(year,
                     pulmonary_deaths, pulmonary_death_rate_per_100k)) %>%
  mutate(percent_pulmonary = percent(pulmonary_notifications/(total_notifications ), accuracy=0.1)) %>%
  mutate(across(where(is.numeric) & !(year),  ~round(., digits=1))) %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.)))
Joining with `by = join_by(year)`Joining with `by = join_by(year)`

Comparison fo age-sex distribution of cases in 1950-1956 vs. 1957


label_abs2 <- function(x) {
  percent(abs(x))
}



cases_by_age_sex %>% 
  ungroup() %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(acf_period, age, sex) %>%
  summarise(cases = sum(cases)) %>%
  ungroup() %>%
  group_by(acf_period) %>%
  mutate(period_total = sum(cases)) %>%
  mutate(pct = cases/period_total) %>%
  mutate(pct2 = case_when(sex=="F" ~ -pct,
                          TRUE ~ pct)) %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
                 mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Pre-ACF",
                                               acf_period=="b. acf" ~ "ACF",
                                               acf_period=="c. post-acf" ~ "Post-ACF")) %>%
  ggplot() +
  geom_vline(aes(xintercept=0), linetype=2) +
  geom_point(aes(x=pct2,y=age, colour=fct_relevel(acf_period,
                                                             "Pre-ACF",
                                                             "ACF",
                                                             "Post-ACF")), stat="identity") +
  scale_x_continuous(labels=label_abs2, limits = c(-0.2, 0.2)) +
  scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA")) +
  theme_grey(base_family = "Aptos") +
  labs(x= "<- Female                Percent of cases              Male ->",
       y="") +
  theme(legend.title = element_blank(),
        legend.position = "bottom")
`summarise()` has grouped output by 'acf_period', 'age'. You can override using the `.groups` argument.
ggsave(here("figures/s5.png"))
Saving 7.29 x 4.5 in image

Prepare the datasets for modelling


mdata <- ward_inc %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(ward) %>%
  mutate(y_num = row_number()) %>%
  ungroup()


mdata_extrapulmonary <- ward_inc %>%
  filter(tb_type=="Non-Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(ward) %>%
  mutate(y_num = row_number()) %>%
  ungroup() %>% 
  filter(year<=1961) #no data for 1962 and 1963


#scaffold for overall predictions
overall_scaffold <- mdata %>%
    select(year, year2, y_num, acf_period, population_without_inst_ship, ward, cases) %>%
    group_by(year, year2, y_num, acf_period) %>%
    summarise(population_without_inst_ship = sum(population_without_inst_ship),
              cases = sum(cases)) %>%
    ungroup() %>%
    mutate(inc_100k = cases/population_without_inst_ship*100000) %>%
    left_join(mdata_extrapulmonary %>% group_by(year) %>%
                summarise(cases_extrapulmonary = sum(cases))) %>%
    mutate(inc_100k_extrapulmonary = cases_extrapulmonary/population_without_inst_ship*100000)
`summarise()` has grouped output by 'year', 'year2', 'y_num'. You can override using the `.groups` argument.Joining with `by = join_by(year)`

7. Pulmonary TB model

7.1 Fit the model and priors

This models the case notification rate over time, with a step change for the intervention, and slope change after the intervention.

Work on the priors a bit. We will build up from less complex to more complex.

  1. intercept only, to predict count of cases

at the intercept, we expect somewhere around 2500. We will set the standard deviation to both 0.5 and 1 to check what it looks like

# 
# c(prior(lognormal(7.600902, 0.5)), #log(2500) = 7.600902
#   prior(lognormal(7.600902, 1))) %>% 
#   parse_dist() %>% 
#   
#   ggplot(aes(y = prior, dist = .dist, args = .args)) +
#   stat_halfeye(.width = c(.5, .95)) +
#   scale_y_discrete(NULL, labels = str_c("lognormal(log(2000), ", c(0.5, 1), ")"),
#                    expand = expansion(add = 0.1)) +
#   xlab(expression(exp(italic(p)(beta[0])))) +
#   coord_cartesian(xlim = c(0,15000))
# 
# 
# prior(gamma(1, 0.01)) %>%
#   parse_dist() %>%
#   ggplot(aes(y=prior, dist = .dist, args = .args)) +
#   stat_halfeye(.width = c(0.5, 0.95))
# 
# #now fit to a model, and plot some prior realisations
# 
# m_prior1 <- brm(
#   cases ~ 0 + Intercept,
#   family = negbinomial(),
#   data = overall_scaffold,
#   sample_prior = "only",
#   prior = prior(normal(log(2000), 0.5), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape)
# )
# 
# add_epred_draws(object=m_prior1,
#                 newdata = tibble(intercept=1)) %>%
#   ggplot(aes(x=intercept, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(labels = comma)

Now try to add in a term for the effect of y_num. We anticpate that the number of cases will decline by about 1-5% per year. However, as we are pretty uncertain about this, we will just encode a weakly regularising prior to restrict the year size to sensible ranges.

# 
# 
# m_prior2 <- brm(
#   cases ~ 0 + Intercept + y_num,
#   family = negbinomial(),
#   data = overall_scaffold,
#   sample_prior = "only",
#   prior = prior(normal(log(2000), 0.5), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b, coef = y_num)
# )
# 
# add_epred_draws(object=m_prior2,
#                 newdata = overall_scaffold) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma)

Now we want to add in a prior for the effect of the acf_intervention. We anticipate the peak to be anywhere between no effect, and a tripling

# 
# m_prior3 <- brm(
#   cases ~ 0 + Intercept + y_num + acf_period,
#   family = negbinomial(),
#   data = overall_scaffold,
#   sample_prior = "only",
#   prior = prior(normal(log(2000), 0.5), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b, coef = y_num) +
#           prior(normal(0, 0.001), class = b)
# )
# 
# 
# add_epred_draws(object=m_prior3,
#                 newdata = overall_scaffold) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(labels = comma)
# 

Now we look and see what it looks like with the interactions

# 
# m_prior4 <- brm(
#   cases ~ 0 + Intercept + y_num + acf_period + y_num:acf_period,
#   family = negbinomial(),
#   data = overall_scaffold,
#   sample_prior = "only",
#   prior = prior(normal(log(2500), 1), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b)
# )
# 
# add_epred_draws(object=m_prior4,
#                 newdata = overall_scaffold) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma)
# 
# 

Now try adding in the random intercepts


# c(prior(lognormal(3.912023, 0.5)), #log(50) = 3.912023
#   prior(lognormal(3.912023, 1))) %>% 
#   parse_dist() %>% 
#   
#   ggplot(aes(y = prior, dist = .dist, args = .args)) +
#   stat_halfeye(.width = c(.5, .95)) +
#   scale_y_discrete(NULL, labels = str_c("lognormal(log(50), ", c(0.5, 1), ")"),
#                    expand = expansion(add = 0.1)) +
#   xlab(expression(exp(italic(p)(beta[0])))) +
#   coord_cartesian(xlim = c(0,400))
# 
# 
# m_prior5 <- brm(
#   cases ~ y_num + acf_period + y_num:acf_period + ( 1 | ward),
#   family = negbinomial(),
#   data = mdata,
#   sample_prior = "only",
#   prior = prior(normal(log(50), 1), class = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b) +
#           prior(exponential(1), class=sd)
# )
# 
# 
# add_epred_draws(object=m_prior5,
#                 newdata = mdata,
#                 re_formula = NA) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma)
# 
# add_epred_draws(object=m_prior5,
#                 newdata = mdata,
#                 re_formula = NA) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma) +
#   facet_wrap(ward~.)

And add in the random slopes

# 
# m_prior6 <- brm(
#   cases ~ 1 + y_num + acf_period + y_num:acf_period + (1 + y_num*acf_period | ward),
#   family = negbinomial(),
#   data = mdata,
#   sample_prior = "only",
#   prior = prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.1), class = b) +
#           prior(exponential(1), class=sd) +
#           prior(lkj(2), class=cor)
# )
# 
# 
# 
# m_prior6 <- brm(
#   cases ~ 0 + Intercept + y_num + acf_period + y_num:acf_period + ( y_num*acf_period | ward),
#   family = negbinomial(),
#   data = mdata,
#   sample_prior = "only",
#   prior = prior(normal(log(50), 1), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b) +
#           prior(exponential(100), class=sd) +
#           prior(lkj(2), class=cor)
# )


# add_epred_draws(object=m_prior6,
#                 newdata = mdata,
#                 re_formula = NA) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma)
# 
# add_epred_draws(object=m_prior6,
#                 newdata = mdata,
#                 re_formula = ~( 1 + y_num + acf_period | ward)) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma) +
#   facet_wrap(ward~.)
# 
# plot_counterfactual(model_data = overall_scaffold, model=m_prior6, outcome = inc_100k, 
#                     population_denominator = population_without_inst_ship, re_formula = NA)
# 
# plot_counterfactual(model_data = mdata, model=m_prior6, outcome = inc_100k, 
#                     population_denominator = population_without_inst_ship, grouping_var = ward, ward,
#                     re_formula = ~( 1 + y_num + acf_period | ward))

Issue here is the non-centred parameterisation of the intercept prior… Feel like this is a more interpretable way to set priors… but will revert to centred parameterisation for the meantime.

# m_centered_prior <- brm(
#   cases ~ 1 + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)),
#                   data = mdata,
#                   family = negbinomial(),
#                   seed = 1234,
#                   chains = 4, cores = 4,
#                   prior = prior(normal(0,1000), class = Intercept) +
#                           prior(gamma(0.01, 0.01), class = shape) +
#                           prior(normal(0, 1), class = b) +
#                           prior(exponential(1), class=sd) +
#                           prior(lkj(2), class=cor),
#                   sample_prior = "only")
# 
# plot(m_centered_prior)
# 
# plot_counterfactual(model_data = overall_scaffold, model=m_centered_prior, outcome = inc_100k, 
#                     population_denominator = population_without_inst_ship, re_formula = NA)
# 
# plot_counterfactual(model_data = mdata, model=m_centered_prior, outcome = inc_100k, 
#                     population_denominator = population_without_inst_ship, grouping_var = ward, ward,
#                     re_formula = ~( 1 + y_num*acf_period | ward))

Look at the mean and variance of counts (counts of pulmonary notifications are what we are predicting)


#Mean of counts per year
mean(mdata$cases)
[1] 48.32819
#variance of counts per year
var(mdata$cases)
[1] 915.5749

Quite a bit of over-dispersion here, so negative binomial distribution might be a better choice of distributional family than Poisson.

Fit the model with the data


m_pulmonary <- brm(
  cases ~ 0 + Intercept + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)),
                  data = mdata,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4,
                  prior = prior(normal(0,1), class=b, coef = "Intercept") +
                          prior(gamma(0.01, 0.01), class = shape) +
                          prior(normal(0, 1), class = b) +
                          prior(exponential(1), class=sd) +
                          prior(lkj(4), class=cor),
  control = list(adapt_delta = 0.9))
Compiling Stan program...
Start sampling
starting worker pid=5049 on localhost:11567 at 16:50:13.533
starting worker pid=5070 on localhost:11567 at 16:50:13.746
starting worker pid=5084 on localhost:11567 at 16:50:13.932
starting worker pid=5098 on localhost:11567 at 16:50:14.111
Error in serialize(data, node$con, xdr = FALSE) : 
  error writing to connection

Nicer version of trace plots for supplemental material

as_draws_df(m_pulmonary) %>% 
  bayesplot::mcmc_rank_overlay(pars = vars(b_Intercept:shape),
             facet_args = list(ncol = 4)) +
  scale_colour_scico_d(palette = "managua", name = "Chain") +
  theme_ggdist()+
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        legend.position = "top")
Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.

Nicer version of table of parameters for supplement


summarise_draws(m_pulmonary) %>%
  mutate(across(c(mean:ess_tail), comma, accuracy=0.01)) %>%
  write_csv(here("figures/s1_table.csv"))
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `across(c(mean:ess_tail), comma, accuracy = 0.01)`.
Caused by warning:
! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
Supply arguments directly to `.fns` through an anonymous function instead.

  # Previously
  across(a:b, mean, na.rm = TRUE)

  # Now
  across(a:b, \(x) mean(x, na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

7.2 Summarise change in CNRs

Summarise the posterior in graphical form


f1b <- plot_counterfactual(model_data = overall_scaffold, model = m_pulmonary, 
                           population_denominator = population_without_inst_ship, outcome = inc_100k, grouping_var=NULL,
                           re_formula = NA)
  
f1b

Make this into a figure combined with the map of empirical data


f1a <- st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
  filter(tb_type=="Pulmonary") %>%
  ggplot() +
  geom_sf(aes(fill=inc_100k), colour="grey98", lwd=0.01) +
  facet_wrap(year~., ncol = 7) +
  scale_fill_scico(name="CNR (per 100,000)",
                       palette = "acton", direction = -1) +
  theme_grey() +
  theme(legend.position = "top",
        #legend.key.width = unit(1, "cm"),
        legend.title.align = 0.5,
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        panel.background = element_blank(),
        legend.title = element_text(size=10))
Joining with `by = join_by(division, ward, ward_number)`Warning: The `legend.title.align` argument of `theme()` is deprecated as of ggplot2 3.5.0.
Please use theme(legend.title = element_text(hjust)) instead.
(f1a / f1b) + plot_annotation(tag_levels = "A")

ggsave(here("figures/f1.png"), width=7, height=8)

Summary of change in notifications numerically


overall_change <- summarise_change(model_data=overall_scaffold, model=m_pulmonary, 
                                   population_denominator=population_without_inst_ship, grouping_var=NULL, re_formula = NA)
`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.
#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
overall_change %>%
  keep((names(.) %in% tokeep)) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()
NA
NA

7.3 Compared to counterfactual

Numbers of pulmonary TB cases averted compared to counterfactual per year.


overall_pulmonary_counterf <- calculate_counterfactual(model_data = overall_scaffold, model=m_pulmonary, population_denominator = population_without_inst_ship)
Joining with `by = join_by(year, population_without_inst_ship, .draw)`Joining with `by = join_by(.draw)`
overall_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()
NA
NA

Total pulmonary TB cases averted between 1958 and 1963


overall_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()
NA
NA

7.4 Correlation between RR.peak, RR.level, and RR.slope

What are the correlations between peak, level, and slope?

7.5 Ward level pulmonary TB estimates

Plot the counterfactual at ward level

Summary of change in notifications at ward level

Calculate the counterfactual per ward


ward_pulmonary_counterf <- calculate_counterfactual(model_data = mdata, model=m_pulmonary, 
                                                    population_denominator = population_without_inst_ship,
                                                    grouping_var = ward, re_formula=~(1 + y_num*acf_period | ward))
`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.Joining with `by = join_by(year, population_without_inst_ship, .draw, ward)`Joining with `by = join_by(.draw, ward)`
ward_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()
NA
NA

Overall counterfactual per ward


ward_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()
NA

8. Extra-pulmonary TB notifications

Now we will model the extra-pulmonary TB notification rate. Struggling a bit with negative binomial model, so revert to Poisson.

8.1 Fit the model


m_extrapulmonary <- brm(
  cases ~ 1 + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)),
                  data = mdata_extrapulmonary,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4,
                  prior = prior(normal(0,1000), class = Intercept) +
                          prior(gamma(0.01, 0.01), class = shape) +
                          prior(normal(0, 1), class = b) +
                          prior(exponential(1), class=sd) +
                          prior(lkj(2), class=cor))
Compiling Stan program...
Start sampling
starting worker pid=98124 on localhost:11567 at 11:02:29.576
starting worker pid=98138 on localhost:11567 at 11:02:29.840
starting worker pid=98152 on localhost:11567 at 11:02:30.070
starting worker pid=98167 on localhost:11567 at 11:02:30.303

SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
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Chain 1: Adjust your expectations accordingly!
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
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Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
Running the chains for more iterations may help. See
https://mc-stan.org/misc/warnings.html#bulk-ess
summary(m_extrapulmonary)
 Family: negbinomial 
  Links: mu = log; shape = identity 
Formula: cases ~ 1 + y_num * acf_period + (1 + y_num * acf_period | ward) + offset(log(population_without_inst_ship)) 
   Data: mdata_extrapulmonary (Number of observations: 444) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Multilevel Hyperparameters:
~ward (Number of levels: 37) 
                                                      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)                                             0.33      0.06     0.23     0.46 1.00     1365     2322
sd(y_num)                                                 0.02      0.01     0.00     0.04 1.01      384     1066
sd(acf_periodb.acf)                                       0.11      0.09     0.00     0.33 1.00     2084     1637
sd(acf_periodc.postMacf)                                  0.12      0.09     0.01     0.34 1.01     1322     1811
sd(y_num:acf_periodb.acf)                                 0.01      0.01     0.00     0.04 1.00     1909     1615
sd(y_num:acf_periodc.postMacf)                            0.01      0.01     0.00     0.04 1.00      906     1267
cor(Intercept,y_num)                                     -0.12      0.31    -0.66     0.53 1.00     1969     2669
cor(Intercept,acf_periodb.acf)                           -0.01      0.33    -0.64     0.62 1.00     5360     3024
cor(y_num,acf_periodb.acf)                               -0.00      0.33    -0.65     0.62 1.00     4082     2934
cor(Intercept,acf_periodc.postMacf)                      -0.07      0.32    -0.65     0.57 1.00     4841     2964
cor(y_num,acf_periodc.postMacf)                          -0.04      0.33    -0.64     0.59 1.00     3930     3058
cor(acf_periodb.acf,acf_periodc.postMacf)                 0.03      0.33    -0.62     0.66 1.00     2814     2651
cor(Intercept,y_num:acf_periodb.acf)                     -0.01      0.33    -0.63     0.63 1.00     5389     3013
cor(y_num,y_num:acf_periodb.acf)                         -0.02      0.33    -0.62     0.60 1.00     3765     3248
cor(acf_periodb.acf,y_num:acf_periodb.acf)               -0.08      0.35    -0.71     0.59 1.00     3816     3404
cor(acf_periodc.postMacf,y_num:acf_periodb.acf)           0.01      0.33    -0.62     0.63 1.00     3414     3206
cor(Intercept,y_num:acf_periodc.postMacf)                -0.14      0.32    -0.69     0.51 1.00     3844     2636
cor(y_num,y_num:acf_periodc.postMacf)                    -0.05      0.33    -0.65     0.59 1.00     3222     2957
cor(acf_periodb.acf,y_num:acf_periodc.postMacf)           0.02      0.33    -0.59     0.64 1.00     2776     3075
cor(acf_periodc.postMacf,y_num:acf_periodc.postMacf)     -0.08      0.35    -0.72     0.60 1.00     2433     2632
cor(y_num:acf_periodb.acf,y_num:acf_periodc.postMacf)     0.02      0.33    -0.61     0.64 1.00     2168     3141

Regression Coefficients:
                           Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept                     -7.93      0.08    -8.08    -7.78 1.00     1561     2465
y_num                         -0.09      0.01    -0.11    -0.06 1.00     4286     2639
acf_periodb.acf               -0.00      0.97    -1.94     1.87 1.00     2620     3110
acf_periodc.postMacf          -0.33      0.40    -1.12     0.47 1.00     2470     2788
y_num:acf_periodb.acf         -0.01      0.12    -0.25     0.23 1.00     2628     3187
y_num:acf_periodc.postMacf     0.02      0.04    -0.06     0.09 1.00     2251     2732

Further Distributional Parameters:
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape    93.62     65.60    27.13   273.55 1.00     4326     3542

Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
plot(m_extrapulmonary)

pp_check(m_extrapulmonary, type='ecdf_overlay')
Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

8.2 Summary of change

Summarise in plot

ggsave(here("figures/s9.png"), width=10)
Saving 10 x 7 in image

Summarise numerically.


overall_change_extrapulmonary <- summarise_change(model_data=overall_scaffold, model=m_extrapulmonary, 
                                   population_denominator=population_without_inst_ship, grouping_var=NULL, re_formula = NA)
`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.
#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
overall_change_extrapulmonary %>%
  keep(names(.) %in% tokeep) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()
NA

8.3 Compared to counterfactual

Numbers of extra-pulmonary TB cases averted overall.


overall_ep_counterf <- calculate_counterfactual(model_data = mdata_extrapulmonary, model=m_extrapulmonary, 
                                               population_denominator = population_without_inst_ship)
Joining with `by = join_by(year, population_without_inst_ship, .draw)`Joining with `by = join_by(.draw)`
overall_ep_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()
NA

Total extrapulmonary TB cases averted between 1958 and 1963


overall_ep_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()
NA
NA

Make into Table 2

bind_rows(
overall_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "PTB_ward"),

overall_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "PTB_overall"),

overall_ep_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "EPTB"),

overall_ep_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "EPTB overall")

) %>%
  select(model, year, diff_inc100k, diff_inc100k.lower:rr_inc100k.upper, 
         cases_averted:cases_averted.upper,
         pct_change:pct_change.upper) %>%
  transmute(model=model, year=year,
            diff_cnr = glue("{diff_inc100k} ({diff_inc100k.lower} to {diff_inc100k.upper})"),
            rr = glue("{rr_inc100k} ({rr_inc100k.lower} to {rr_inc100k.upper})"),
            cases_averted = glue("{cases_averted} ({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue("{pct_change} ({pct_change.lower} to {pct_change.upper})")) %>%
  write_csv(here("figures/table2.csv"))

8.4 Ward-level extra-pulmonary summaries

Ward-level extra-pulmonary estimates in graphical form.

plot_counterfactual(model_data = mdata_extrapulmonary, model=m_extrapulmonary, outcome = inc_100k, 
                    population_denominator = population_without_inst_ship, grouping_var = ward,re_formula =~(y_num*acf_period | ward), 
                    ward) + scale_y_continuous(limits= c(0,75))
Scale for y is already present.
Adding another scale for y, which will replace the existing scale.

Numerical summary.


ward_change_extrapulmonary <- summarise_change(model_data = mdata_extrapulmonary, model = m_extrapulmonary, 
                                population_denominator = population_without_inst_ship, grouping_var=ward,
                                re_formula = ~(y_num*acf_period | ward)) 
`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw', 'y_num'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw'. You can override using the `.groups` argument.
#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
ward_change_extrapulmonary  %>%
  keep(names(.) %in% tokeep) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()
NA
NA
NA

9. Age-sex model

9.1 FIt the model

Fit the model

(Not rewritten the functions for this yet)


mdata_age_sex <- cases_by_age_sex %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  mutate(year2 = year+0.5) %>%
  group_by(age, sex) %>%
  mutate(y_num = row_number()) %>%
  ungroup()

m_age_sex <- brm(
  cases ~ y_num + (acf_period)*(age*sex) + (acf_period:y_num)*(age*sex),
                  data = mdata_age_sex,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = prior(normal(0,1), class = Intercept) +
                          prior(gamma(0.01, 0.01), class = shape) +
                          prior(normal(0, 1), class = b))
Compiling Stan program...
Start sampling
starting worker pid=98428 on localhost:11567 at 11:05:18.993
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SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
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Chain 1: Adjust your expectations accordingly!
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Chain 4: 
Chain 4:  Elapsed Time: 16.959 seconds (Warm-up)
Chain 4:                18.942 seconds (Sampling)
Chain 4:                35.901 seconds (Total)
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Chain 3: 
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Chain 2: 
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Chain 1:                20.185 seconds (Sampling)
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Chain 1: 
summary(m_age_sex)
 Family: negbinomial 
  Links: mu = log; shape = identity 
Formula: cases ~ y_num + (acf_period) * (age * sex) + (acf_period:y_num) * (age * sex) 
   Data: mdata_age_sex (Number of observations: 224) 
  Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
         total post-warmup draws = 4000

Regression Coefficients:
                                         Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept                                    4.42      0.11     4.20     4.64 1.00     1397     2401
y_num                                       -0.17      0.03    -0.23    -0.11 1.00     1352     2186
acf_periodb.acf                             -0.02      0.99    -1.98     1.94 1.00     6206     2892
acf_periodc.postMacf                        -0.50      0.33    -1.15     0.15 1.00     2432     2868
age06_15                                     0.63      0.15     0.34     0.93 1.00     1844     2734
age16_25                                     1.85      0.13     1.60     2.12 1.00     1587     2376
age26_35                                     1.13      0.14     0.86     1.40 1.00     1700     2530
age36_45                                     0.28      0.15    -0.01     0.57 1.00     1880     2731
age46_55                                    -0.62      0.17    -0.97    -0.27 1.00     2170     2602
age56_65                                    -1.08      0.19    -1.46    -0.71 1.00     2551     2927
age65P                                      -1.64      0.22    -2.07    -1.21 1.00     2964     3032
sexM                                         0.16      0.15    -0.13     0.46 1.00     1413     2537
age06_15:sexM                               -0.43      0.21    -0.83    -0.03 1.00     2045     2796
age16_25:sexM                               -0.58      0.18    -0.94    -0.23 1.00     1713     2556
age26_35:sexM                               -0.33      0.19    -0.70     0.04 1.00     1668     2681
age36_45:sexM                                0.24      0.20    -0.16     0.62 1.00     1941     2649
age46_55:sexM                                1.15      0.22     0.72     1.57 1.00     1947     2397
age56_65:sexM                                1.13      0.24     0.67     1.59 1.00     2309     2597
age65P:sexM                                  1.00      0.26     0.47     1.50 1.00     2723     3163
acf_periodb.acf:age06_15                     0.01      1.00    -1.95     1.95 1.00     7072     2973
acf_periodc.postMacf:age06_15               -0.59      0.51    -1.57     0.45 1.00     3737     3352
acf_periodb.acf:age16_25                     0.04      0.98    -1.88     1.90 1.00     6437     2778
acf_periodc.postMacf:age16_25                0.74      0.42    -0.08     1.58 1.00     3367     3236
acf_periodb.acf:age26_35                     0.05      1.01    -1.95     2.01 1.00     6205     2400
acf_periodc.postMacf:age26_35                0.66      0.43    -0.19     1.50 1.00     3365     3163
acf_periodb.acf:age36_45                     0.04      1.03    -2.00     2.07 1.00     6355     2766
acf_periodc.postMacf:age36_45                0.75      0.46    -0.15     1.66 1.00     3567     3174
acf_periodb.acf:age46_55                     0.06      0.98    -1.86     1.95 1.00     7318     2828
acf_periodc.postMacf:age46_55                0.86      0.48    -0.06     1.78 1.00     3668     2624
acf_periodb.acf:age56_65                     0.03      0.99    -1.84     1.96 1.00     7096     2983
acf_periodc.postMacf:age56_65                0.63      0.52    -0.36     1.64 1.00     3886     2904
acf_periodb.acf:age65P                       0.06      0.97    -1.81     1.96 1.00     7553     3091
acf_periodc.postMacf:age65P                  0.97      0.54    -0.11     1.99 1.00     3746     2598
acf_periodb.acf:sexM                        -0.00      1.01    -2.00     1.96 1.00     6464     3241
acf_periodc.postMacf:sexM                   -0.06      0.37    -0.78     0.66 1.00     2954     3273
y_num:acf_periodb.acf                       -0.10      0.13    -0.36     0.16 1.00     5945     2661
y_num:acf_periodc.postMacf                   0.04      0.04    -0.03     0.12 1.00     2137     2900
acf_periodb.acf:age06_15:sexM                0.01      0.98    -1.95     1.93 1.00     7778     3164
acf_periodc.postMacf:age06_15:sexM          -0.58      0.63    -1.80     0.65 1.00     4705     2901
acf_periodb.acf:age16_25:sexM                0.01      0.96    -1.89     1.90 1.00     6657     2848
acf_periodc.postMacf:age16_25:sexM           0.65      0.53    -0.36     1.69 1.00     4256     3449
acf_periodb.acf:age26_35:sexM               -0.01      1.01    -1.95     1.95 1.00     6234     2832
acf_periodc.postMacf:age26_35:sexM           0.40      0.52    -0.61     1.40 1.00     3754     3322
acf_periodb.acf:age36_45:sexM                0.00      0.98    -1.89     1.88 1.00     7630     2767
acf_periodc.postMacf:age36_45:sexM           0.10      0.55    -0.97     1.20 1.00     4806     3262
acf_periodb.acf:age46_55:sexM                0.00      0.99    -1.99     1.94 1.00     6639     3139
acf_periodc.postMacf:age46_55:sexM           0.66      0.54    -0.40     1.70 1.00     4437     2911
acf_periodb.acf:age56_65:sexM                0.02      0.97    -1.87     1.92 1.00     7289     2921
acf_periodc.postMacf:age56_65:sexM           0.34      0.57    -0.74     1.46 1.00     4341     3319
acf_periodb.acf:age65P:sexM                  0.04      0.98    -1.86     1.95 1.00     6525     3240
acf_periodc.postMacf:age65P:sexM             0.27      0.60    -0.91     1.46 1.00     4599     3141
y_num:acf_perioda.preMacf:age06_15           0.02      0.04    -0.05     0.10 1.00     1656     2702
y_num:acf_periodb.acf:age06_15               0.15      0.13    -0.11     0.42 1.00     6384     2890
y_num:acf_periodc.postMacf:age06_15          0.08      0.05    -0.02     0.17 1.00     3217     3004
y_num:acf_perioda.preMacf:age16_25           0.12      0.03     0.06     0.19 1.00     1498     2372
y_num:acf_periodb.acf:age16_25               0.25      0.13    -0.01     0.50 1.00     6014     2846
y_num:acf_periodc.postMacf:age16_25         -0.04      0.04    -0.12     0.04 1.00     2736     3384
y_num:acf_perioda.preMacf:age26_35           0.15      0.03     0.08     0.22 1.00     1593     2508
y_num:acf_periodb.acf:age26_35               0.31      0.14     0.05     0.59 1.00     5080     2449
y_num:acf_periodc.postMacf:age26_35          0.02      0.04    -0.06     0.10 1.00     2836     2985
y_num:acf_perioda.preMacf:age36_45           0.17      0.04     0.10     0.24 1.00     1676     2526
y_num:acf_periodb.acf:age36_45               0.40      0.14     0.14     0.67 1.00     6032     3088
y_num:acf_periodc.postMacf:age36_45          0.06      0.04    -0.02     0.14 1.00     2986     3068
y_num:acf_perioda.preMacf:age46_55           0.19      0.04     0.11     0.28 1.00     1857     2753
y_num:acf_periodb.acf:age46_55               0.44      0.13     0.18     0.70 1.00     6515     2608
y_num:acf_periodc.postMacf:age46_55          0.09      0.04     0.01     0.18 1.00     3184     3159
y_num:acf_perioda.preMacf:age56_65           0.17      0.05     0.08     0.27 1.00     2082     2571
y_num:acf_periodb.acf:age56_65               0.39      0.13     0.14     0.64 1.00     6358     3037
y_num:acf_periodc.postMacf:age56_65          0.11      0.05     0.02     0.20 1.00     3033     3210
y_num:acf_perioda.preMacf:age65P             0.23      0.05     0.13     0.33 1.00     2645     2713
y_num:acf_periodb.acf:age65P                 0.43      0.13     0.18     0.68 1.00     7083     3005
y_num:acf_periodc.postMacf:age65P            0.11      0.05     0.02     0.20 1.00     3171     2781
y_num:acf_perioda.preMacf:sexM               0.02      0.04    -0.06     0.09 1.00     1333     2367
y_num:acf_periodb.acf:sexM                  -0.04      0.14    -0.31     0.24 1.00     6215     3021
y_num:acf_periodc.postMacf:sexM             -0.01      0.04    -0.09     0.06 1.00     2254     2895
y_num:acf_perioda.preMacf:age06_15:sexM      0.00      0.05    -0.10     0.10 1.00     1870     2862
y_num:acf_periodb.acf:age06_15:sexM          0.04      0.14    -0.23     0.33 1.00     6489     2838
y_num:acf_periodc.postMacf:age06_15:sexM     0.10      0.06    -0.01     0.21 1.00     3508     2947
y_num:acf_perioda.preMacf:age16_25:sexM     -0.00      0.05    -0.09     0.09 1.00     1633     2322
y_num:acf_periodb.acf:age16_25:sexM          0.06      0.13    -0.21     0.32 1.00     5721     2812
y_num:acf_periodc.postMacf:age16_25:sexM    -0.01      0.05    -0.11     0.09 1.00     3214     3072
y_num:acf_perioda.preMacf:age26_35:sexM     -0.01      0.05    -0.10     0.08 1.00     1558     2600
y_num:acf_periodb.acf:age26_35:sexM          0.05      0.14    -0.23     0.33 1.00     5835     3066
y_num:acf_periodc.postMacf:age26_35:sexM    -0.00      0.05    -0.10     0.09 1.00     2801     2922
y_num:acf_perioda.preMacf:age36_45:sexM     -0.01      0.05    -0.11     0.08 1.00     1678     2542
y_num:acf_periodb.acf:age36_45:sexM          0.00      0.14    -0.26     0.27 1.00     5495     2932
y_num:acf_periodc.postMacf:age36_45:sexM    -0.00      0.05    -0.10     0.10 1.00     3458     3304
y_num:acf_perioda.preMacf:age46_55:sexM     -0.01      0.05    -0.11     0.10 1.00     1708     2607
y_num:acf_periodb.acf:age46_55:sexM         -0.00      0.14    -0.28     0.28 1.00     5719     2840
y_num:acf_periodc.postMacf:age46_55:sexM    -0.06      0.05    -0.17     0.04 1.00     3314     2999
y_num:acf_perioda.preMacf:age56_65:sexM      0.05      0.06    -0.06     0.17 1.00     1993     2399
y_num:acf_periodb.acf:age56_65:sexM          0.07      0.14    -0.19     0.35 1.00     6147     2930
y_num:acf_periodc.postMacf:age56_65:sexM     0.02      0.05    -0.09     0.12 1.00     3245     3092
y_num:acf_perioda.preMacf:age65P:sexM        0.00      0.06    -0.11     0.13 1.00     2469     3062
y_num:acf_periodb.acf:age65P:sexM            0.07      0.14    -0.20     0.34 1.01     5209     3126
y_num:acf_periodc.postMacf:age65P:sexM       0.01      0.06    -0.10     0.12 1.00     3625     2882

Further Distributional Parameters:
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape   202.57     68.03   108.42   370.40 1.00     2430     2563

Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
plot(m_age_sex)

pp_check(m_age_sex, type='ecdf_overlay')
Using 10 posterior draws for ppc type 'ecdf_overlay' by default.

Summarise posterior

ggsave(here("figures/s12.png"), height=10)
Saving 7 x 10 in image

9.2 Summary of impact of intervention

Calculate summary effects


#Peak
out_age_sex_1 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num == 8),
                      acf_period = c("a. pre-acf", "b. acf"))

peak_draws_age_sex <- add_epred_draws(newdata = out_age_sex_1,
                  object = m_age_sex) %>%
    group_by(.draw, age, sex) %>%
    summarise(estimate = last(.epred)/first(.epred)) %>%
    ungroup() %>%
    mutate(measure = "RR.peak")
`summarise()` has grouped output by '.draw', 'age'. You can override using the `.groups` argument.
  
peak_summary_age_sex <- peak_draws_age_sex %>%
    group_by(age, sex) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.peak")


#Level
 
out_age_sex_2 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num == 9),
                      acf_period = c("a. pre-acf", "c. post-acf"))
  
level_draws_age_sex <- add_epred_draws(newdata = out_age_sex_2,
                  object = m_age_sex) %>%
    arrange(y_num, .draw) %>%
    group_by(.draw, age, sex) %>%
    summarise(estimate = last(.epred)/first(.epred)) %>%
    ungroup() %>%
    mutate(measure = "RR.level")
`summarise()` has grouped output by '.draw', 'age'. You can override using the `.groups` argument.
  
level_summary_age_sex <- level_draws_age_sex %>%
    group_by(age, sex) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.level")

#Slope

out_age_sex_3 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf"))
  
slope_draws_age_sex <- add_epred_draws(newdata = out_age_sex_3,
                  object = m_age_sex) %>%
        arrange(y_num) %>%
        ungroup() %>%
        group_by(.draw, y_num, age, sex) %>%
        summarise(slope = last(.epred)/first(.epred)) %>%
        ungroup() %>%
        group_by(.draw, age, sex) %>%
        summarise(estimate = last(slope)/first(slope)) %>%
        mutate(measure = "RR.slope")
`summarise()` has grouped output by '.draw', 'y_num', 'age'. You can override using the `.groups` argument.`summarise()` has grouped output by '.draw', 'age'. You can override using the `.groups` argument.
  
slope_summary_age_sex <- slope_draws_age_sex %>%
     group_by(age, sex) %>%
      median_qi(estimate) %>%
      mutate(measure = "RR.slope")

Numerical summary of these summary results


bind_rows(
  peak_summary_age_sex, level_summary_age_sex, slope_summary_age_sex
) %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()
NA
NA
NA

As a figure


peak_g_age_sex <- peak_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

#level plot
level_g_age_sex <- level_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

#slope plot
slope_g_age_sex <- slope_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

9.3 Compared to counterfactual


counterfact_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex) %>%
                                    mutate(acf_period = "a. pre-acf")) %>%
      filter(year>1957) %>%
      select(year, age, sex, .draw, .epred_counterf = .epred)
Adding missing grouping variables: `year2`, `y_num`, `acf_period`, `.row`
  
#Calcuate predicted number of cases per draw, then summarise.
post_change_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex, acf_period)) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, age, sex, .draw, .epred) 
  
#for the overall period
counterfact_overall_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex) %>%
                                    mutate(acf_period = "a. pre-acf")) %>%
      filter(year>1957) %>%
      select(age, sex, .draw, .epred)  %>%
      group_by(age, sex, .draw) %>%
      summarise(.epred_counterf = sum(.epred)) %>%
      mutate(year = "Overall (1958-1963)")
Adding missing grouping variables: `year`, `year2`, `y_num`, `acf_period`, `.row``summarise()` has grouped output by 'age', 'sex'. You can override using the `.groups` argument.
  
#Calcuate incidence per draw, then summarise.
post_change_overall_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex, acf_period)) %>%
      filter(year>1957) %>%
      select(age, sex, .draw, .epred) %>%
      group_by(.draw, age, sex) %>%
      summarise(.epred = sum(.epred)) 
Adding missing grouping variables: `year`, `year2`, `y_num`, `acf_period`, `.row``summarise()` has grouped output by '.draw', 'age'. You can override using the `.groups` argument.
  
counter_post_overall_age_sex <-
  left_join(counterfact_overall_age_sex, post_change_overall_age_sex) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf) %>%
    group_by(age, sex) %>%
    mean_qi(cases_averted, pct_change) %>%
    ungroup() %>%
    mutate(year = "Overall (1958-1963)") 
Joining with `by = join_by(age, sex, .draw)`
age_sex_txt <- counter_post_overall_age_sex %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  transmute(year = as.character(year),
            sex = sex,
            age = age,
            cases_averted = glue::glue("{cases_averted}\n({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change}\n({pct_change.lower} to {pct_change.upper})"))


age_sex_txt %>% datatable()
NA
NA

counterfactual_g_age_sex <- counter_post_overall_age_sex %>% 
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_pointrange(aes(x = age, y=cases_averted, ymin=cases_averted.lower, ymax=cases_averted.upper, colour=sex, shape=sex), position=position_dodge(width=0.5)) + 
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  scale_y_continuous(labels = comma) +
  labs(x="",
       y="Number (95% UI)",
       colour="") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        legend.position = "bottom")

counterfactual_g_age_sex

Join together for Figure 3.


(peak_g_age_sex + level_g_age_sex) / (slope_g_age_sex + counterfactual_g_age_sex) + plot_annotation(tag_levels = "A") + plot_layout(guides = "collect") & theme(legend.position = "bottom")

ggsave(here("figures/f3.png"), width = 12, height=8)

NA
NA

10 Division model

Was uptake of CXR at division level associated with greated impact?

{r} # # m_division <- brm( # cases ~ 1 + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)), # data = mdata, # family = negbinomial(), # seed = 1234, # chains = 4, cores = 4, # prior = prior(normal(0,1), class = Intercept) + # prior(gamma(0.01, 0.01), class = shape) + # prior(normal(0, 1), class = b) + # prior(exponential(1), class=sd) + # prior(lkj(4), class=cor), # control = list(adapt_delta = 0.9)) # #

---
title: "Glasgow TB ACF"
output: html_notebook
---

### 1. Libraries and functions

#### 1.1 Libraries

Load the required libraries.

```{r, message=F, warning=F}
library(tidyverse)
library(sf)
library(here)
library(readxl)
library(scales)
library(DT)
library(brms)
library(tidybayes)
library(patchwork)
library(marginaleffects)
library(ggrepel)
library(scico)
library(ggdensity)
library(ggpubr)
library(units)
library(glue)
library(ggh4x)

```

#### 1.2 Helper functions

Functions that we will use throughout the script

```{r}
#labeller for years
year_labels <- c(1950:1963)

#The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
#Segment for graphs to match ACF period
acf_start <- decimal_date(ymd("1957-03-11"))
acf_end <- decimal_date(ymd("1957-04-12"))


```

Function for counterfactual plots

```{r}


plot_counterfactual <- function(model_data, model, population_denominator, outcome, grouping_var=NULL, re_formula,...){
  
  #labeller for years
  year_labels <- c(1950:1964) #extra year for the extant of the x-axis

  #The Glasgow mass minuture chest X-ray campaign happened between 11th March and 12th April 1957
  #Segment for graphs to match ACF period
  acf_start <- decimal_date(ymd("1957-03-11"))
  acf_end <- decimal_date(ymd("1957-04-12"))

  summary <- {{model_data}} %>%
    select(year, year2, y_num, acf_period, {{population_denominator}}, {{outcome}}, {{grouping_var}}) %>%
    add_epred_draws({{model}}, re_formula={{re_formula}}) %>%
    group_by(year2, acf_period, {{grouping_var}}) %>%
    mean_qi() %>%
    mutate(.epred_inc = .epred/{{population_denominator}}*100000,
          .epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
          .epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
    mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                  acf_period=="c. post-acf" ~ "Post Intervention"))



  #create the counterfactual (no intervention), and summarise
  
  counterfact <-
    add_epred_draws(object = {{model}},
                    newdata = {{model_data}} %>%
                                  select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, {{outcome}}) %>%
                                  mutate(acf_period = "a. pre-acf"), re_formula={{re_formula}}) %>%
    group_by(year2, acf_period, {{grouping_var}}) %>%
    mean_qi() %>%
    mutate(.epred_inc = .epred/{{population_denominator}}*100000,
         .epred_inc.lower = .epred.lower/{{population_denominator}}*100000,
         .epred_inc.upper = .epred.upper/{{population_denominator}}*100000) %>%
    mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))
  


  #plot the intervention effect
p <- summary %>%
    droplevels() %>%
    ggplot() +
    geom_vline(aes(xintercept=acf_start, linetype="Mass CXR screening intervention")) +
    geom_vline(aes(xintercept=acf_end, linetype="Mass CXR screening intervention")) +
    geom_ribbon(aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
    geom_ribbon(data = counterfact %>% filter(year>=1956), 
                aes(ymin=.epred_inc.lower, ymax=.epred_inc.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
    geom_line(data = counterfact %>% filter(year>=1956), 
              aes(y=.epred_inc, x=year2, colour="Counterfactual")) +
    geom_line(aes(y=.epred_inc, x=year2, group=acf_period,  colour=acf_period)) +
    geom_point(data = {{model_data}} %>%
                 mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Pre-ACF",
                                               acf_period=="b. acf" ~ "ACF",
                                               acf_period=="c. post-acf" ~ "Post-ACF")), 
               aes(y={{outcome}}, x=year2, shape=fct_relevel(acf_period,
                                                             "Pre-ACF",
                                                             "ACF",
                                                             "Post-ACF")), size=2) +
    theme_grey() +
    scale_y_continuous(labels=comma, limits =c(0,400)) +
    scale_x_continuous(labels = year_labels,
                       breaks = year_labels) +
    scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:", na.translate = F) +
    scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:", na.translate = F) +
    scale_shape_discrete(name="Empirical data (period):", na.translate = F) +
    scale_linetype_manual(values = 2, name="") +
    labs(
      x = "",
      y = "CNR (per 100,000)"
    ) +
    guides(x = "axis_truncated", y = "axis_truncated") +
    theme(legend.position = "bottom",
          legend.box="vertical", 
          text = element_text(size=10),
          axis.text.x = element_text(size=10, angle = 90, hjust=1, vjust=0.5),
          legend.text = element_text(size=10),
          legend.spacing.y = unit(0.1, 'cm'),
          axis.line = element_line(colour = "black")) 

    facet_vars <- vars(...)

  if (length(facet_vars) != 0) {
    p <- p + facet_wrap(facet_vars)
  }
  p

}

```

Function for calculating  measures of change over time (RR.peak, RR.level, RR.slope)


```{r}

summarise_change <- function(model_data, model, population_denominator, grouping_var = NULL, re_formula = NULL) {
  
  #functions for calculating RR.peak
  #i.e. relative case notification rate in 1957 vs. counterfactual trend for 1957
  
  grouping_var <- enquo(grouping_var)
  
  if (!is.null({{grouping_var}})) {
    
    #make the prediction matrix, conditional on whether we want random effects included or not.
    out <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num == 8),
                    acf_period = c("a. pre-acf", "b. acf")
    )
  } else {
    
    out <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num == 8),
                    acf_period = c("a. pre-acf", "b. acf")
    )
  }
  
  peak_draws <- add_epred_draws(newdata = out,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
    mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
    group_by(.draw, !!grouping_var) %>%
    summarise(estimate = last(epred_cnr)/first(epred_cnr)) %>%
    ungroup() %>%
    mutate(measure = "RR.peak")
  
  peak_summary <- peak_draws %>%
    group_by(!!grouping_var) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.peak")
  
  
  #functions for calculating RR.level
  #i.e. relative case notification rate in 1958 vs. counterfactual trend for 1958
  
    if (!is.null({{grouping_var}})) {
    out2 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num == 9),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  } else {
    
    out2 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num == 9),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  }
  
    level_draws <- add_epred_draws(newdata = out2,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
    arrange(y_num, .draw) %>%
    mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
    group_by(.draw, !!grouping_var) %>%
    summarise(estimate = last(epred_cnr)/first(epred_cnr)) %>%
    ungroup() %>%
    mutate(measure = "RR.level")
  
  level_summary <- level_draws %>%
    group_by(!!grouping_var) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.level")
    
    
  #functions for calculating RR.slope
  #i.e. relative change in case notification rate in 1958-1963 vs. counterfactual trend for 1959-1963
  
    if (!is.null({{grouping_var}})) {
    out3 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num, !!grouping_var) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  } else {
    
    out3 <- crossing({{model_data}} %>% 
                      select({{population_denominator}}, y_num) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf")
    )
  }
  
    slope_draws <- add_epred_draws(newdata = out3,
                  object = {{model}},
                  re_formula = {{re_formula}}) %>%
        arrange(y_num) %>%
        ungroup() %>%
        mutate(epred_cnr = .epred/population_without_inst_ship*100000) %>%
        group_by(.draw, y_num, !!grouping_var) %>%
        summarise(slope = last(epred_cnr)/first(epred_cnr)) %>%
        ungroup() %>%
        group_by(.draw, !!grouping_var) %>%
        summarise(estimate = last(slope)/first(slope)) %>%
        mutate(measure = "RR.slope")
  
  slope_summary <- slope_draws %>%
     group_by(!!grouping_var) %>%
      mean_qi(estimate) %>%
      mutate(measure = "RR.slope")
    
  #gather all the results into a named list
    lst(peak_draws=peak_draws, peak_summary=peak_summary, 
        level_draws=level_draws, level_summary=level_summary, 
        slope_draws=slope_draws, slope_summary=slope_summary)
  
}

```


Function for calculating difference from counterfactual

```{r}

calculate_counterfactual <- function(model_data, model, population_denominator, grouping_var=NULL, re_formula=NA){
  
  #effect vs. counterfactual
  counterfact <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}) %>%
                                    mutate(acf_period = "a. pre-acf"),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, year, {{grouping_var}}, acf_period) %>%
      mutate(.epred_inc_counterf = .epred/{{population_denominator}}*100000, .epred_counterf=.epred)  %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, {{population_denominator}}, .draw, .epred_counterf, .epred_inc_counterf, {{grouping_var}})
  
  #Calcuate case notification rate per draw, then summarise.
  post_change <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, acf_period),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, year, {{grouping_var}}, acf_period) %>%
      mutate(.epred_inc = .epred/{{population_denominator}}*100000)  %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, {{population_denominator}}, {{grouping_var}}, .draw, .epred, .epred_inc, {{grouping_var}}) 
  
  #for the overall period
    counterfact_overall <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}) %>%
                                    mutate(acf_period = "a. pre-acf"),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, {{grouping_var}}) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select({{population_denominator}}, .draw, .epred, {{grouping_var}})  %>%
      group_by(.draw, {{grouping_var}}) %>%
      summarise(.epred_counterf = sum(.epred)) 
  
  #Calcuate case notification rate per draw, then summarise.
  post_change_overall <-
      add_epred_draws(object = {{model}},
                      newdata = {{model_data}} %>%
                                    select(year, year2, y_num, {{population_denominator}}, {{grouping_var}}, acf_period),
                      re_formula = {{re_formula}}) %>%
      group_by(.draw, {{grouping_var}}) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select({{population_denominator}}, {{grouping_var}}, .draw, .epred) %>%
      group_by(.draw, {{grouping_var}}) %>%
      summarise(.epred = sum(.epred)) 
  
  
counter_post <-
  left_join(counterfact, post_change) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf,
           diff_inc100k = .epred_inc - .epred_inc_counterf,
           rr_inc100k = .epred_inc/.epred_inc_counterf) %>%
    group_by(year, {{grouping_var}}) %>%
    mean_qi(cases_averted, pct_change, diff_inc100k, rr_inc100k) %>%
    ungroup()

counter_post_overall <-
  left_join(counterfact_overall, post_change_overall) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf) %>%
    group_by({{grouping_var}}) %>%
    mean_qi(cases_averted, pct_change) %>%
    ungroup()

lst(counter_post, counter_post_overall)

}


```

Function for tidying up counterfactuals (mostly for making nice tables)

```{r}

tidy_counterfactuals <- function(data){
  data %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(year = as.character(year),
            cases_averted = glue::glue("{cases_averted} ({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change} ({pct_change.lower} to {pct_change.upper})"),
            diff_inc = glue::glue("{diff_inc100k} ({diff_inc100k.lower} to {diff_inc100k.upper})"),
            rr_inc = glue::glue("{rr_inc100k} ({rr_inc100k.lower} to {rr_inc100k.upper})"))
}


tidy_counterfactuals_overall <- function(data){
  data %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(year = as.character(year),
            cases_averted = glue::glue("{cases_averted} ({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change} ({pct_change.lower} to {pct_change.upper})"))
}

```



### 2. Data

Import datasets for analysis

#### 2.1 Shapefiles

Make a map of Glasgow wards

```{r}

glasgow_wards_1951 <- st_read(here("mapping/glasgow_wards_1951.geojson"))

```

```{r}

#read in Scotland boundary
scotland <- st_read(here("mapping/Scotland_boundary/Scotland boundary.shp"))

#make a bounding box for Glasgow
bbox <- st_bbox(glasgow_wards_1951) |> st_as_sfc()

#plot scotland with a bounding box around the City of Glasgow
scotland_with_bbox <- ggplot() +
  geom_sf(data = scotland, fill="antiquewhite") +
  geom_sf(data = bbox, colour = "#C60C30", fill="antiquewhite") +
  theme_void() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA, linewidth = 0.5),
        panel.background = element_rect(fill = "#EAF7FA", size = 0.3))

#plot the wards
#note we tidy up some names to fit on map
glasgow_ward_map <- glasgow_wards_1951 %>%
  mutate(ward = case_when(ward=="Shettleston and Tollcross" ~ "Shettleston and\nTollcross",
                          ward=="Partick (West)" ~ "Partick\n(West)",
                          ward=="Partick (East)" ~ "Partick\n(East)",
                          ward=="North Kelvin" ~ "North\nKelvin",
                          ward=="Kinning Park" ~ "Kinning\nPark",
                          TRUE ~ ward)) %>%
  
  ggplot() +
  geom_sf(aes(fill=division)) +
  geom_sf_label(aes(label = ward), size=3, fill=NA, label.size = NA, colour="black") +
  #scale_colour_identity() +
  scale_fill_brewer(palette = "Set3", name="City of Glasgow Division") +
  theme_grey() +
  labs(x="",
       y="",
       fill="Division") +
  theme(legend.position = "top",
        
        panel.border = element_rect(colour = "grey78", fill=NA, linewidth = 0.5),
        panel.background = element_rect(fill = "antiquewhite", size = 0.3),
        panel.grid.major = element_line(color = "grey78")) +
  guides(fill=guide_legend(title.position = "top", title.hjust = 0.5, title.theme = element_text(face="bold")))

#add the map of scotland as an inset
glasgow_ward_map + inset_element(scotland_with_bbox, 0.75, 0, 1, 0.4)

ggsave(here("figures/s1.png"), height=10, width = 12)


```

Calculate areas per geographical unit

```{r}
sf_use_s2(FALSE) #https://github.com/r-spatial/sf/issues/1762

glasgow_wards_1951 <- glasgow_wards_1951 %>%
  mutate(area = st_area(glasgow_wards_1951))


glasgow_wards_1951$area_km <- units::set_units(glasgow_wards_1951$area, km^2)


```


Make division shape files, and calculate area
(stopped working, need to fix!)

```{r}

# glasgow_divisions_1951 <- glasgow_wards_1951 %>%
#   group_by(division) %>% 
#   summarize(geometry = st_union(geometry)) %>%
#   nngeo::st_remove_holes() %>%
#   mutate(area = st_area(glasgow_divisions_1951))
# 
# glasgow_divisions_1951$area_km <- units::set_units(glasgow_divisions_1951$area, km^2)


```


### 3. Denominators

Load in the datasets for denonomiators, and check for consistency.

```{r}

overall_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "overall_population")

overall_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
overall_pops <- overall_pops %>%
  mutate(year2 = year+0.5)

```

Note, we have three population estimates:

1. Population without institutionalised people or people in shipping
2. Population in institutions
3. Population in shipping

(Population in shipping is estimated from the 1951 census, so is the same for most years)

#### 3.1 Overall population

First, plot the total population

```{r}

overall_pops %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2), alpha=0.5, colour = "mediumseagreen", fill="mediumseagreen") +
  geom_point(aes(y=total_population, x=year2), colour = "mediumseagreen") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(
    title = "Glasgow Corporation: total population",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist()


```

Now the population excluding institutionalised and shipping population

```{r}

overall_pops %>%
  ggplot() +
  geom_area(aes(y=population_without_inst_ship, x=year2), alpha=0.5, colour = "purple", fill="purple") +
  geom_point(aes(y=population_without_inst_ship, x=year2), colour = "purple") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(
    title = "Glasgow Corporation: population excluding institutionalised and shipping",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist()


```

#### 3.2 Population by Ward

There are 5 Divisions containing 37 Wards in the Glasgow Corporation, with consistent boundaries over time.

```{r}
#look-up table for divisions and wards
ward_lookup <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "divisions_wards")


ward_pops <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "ward_population")

ward_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

#shift year to midpoint
ward_pops <- ward_pops %>%
  mutate(year2 = year+0.5)

#Get the Division population
division_pops <- ward_pops %>%
  group_by(division, year) %>%
  summarise(population_without_inst_ship = sum(population_without_inst_ship, na.rm = TRUE),
            institutions = sum(institutions, na.rm = TRUE),
            shipping = sum(shipping, na.rm = TRUE),
            total_population = sum(total_population, na.rm = TRUE))

division_pops %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

```

Plot the overall population by Division and Ward

```{r}

division_pops %>%
  mutate(year2 = year+0.5) %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2, colour=division, fill=division), alpha=0.8) +
  geom_point(aes(y=total_population, x=year2, colour=division)) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  facet_wrap(division~.) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_brewer(palette = "Set3", name = "") +
  scale_colour_brewer(palette = "Set3", name = "") +
  labs(
    title = "Glasgow Corporation: total population by Division",
    subtitle = "1950 to 1963",
    x = "Year",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")


```

```{r}

ward_pops %>%
  ggplot() +
  geom_area(aes(y=total_population, x=year2, colour=division, fill=division)) +
  geom_point(aes(y=total_population, x=year2, colour=division), colour="black") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  facet_wrap(ward~., ncol=6) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_brewer(palette = "Set3", name="Division") +
  scale_colour_brewer(palette = "Set3", name = "Division") +
  labs(
    x = "",
    y = "Population",
    caption = "Mid-year estimates\nMass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

ggsave(here("figures/s2.png"), height=14, width=12)

```

Approximately, how many person-years of follow-up do we have?

```{r}

overall_pops %>%
  ungroup() %>%
  summarise(across(year, length, .names = "years"),
            across(c(population_without_inst_ship, total_population), sum)) %>%
  mutate(across(where(is.double), comma)) %>%
  datatable()


```

Change in population by ward

```{r}

ward_pops %>%
  group_by(ward) %>%
  summarise(pct_change_pop = (last(population_without_inst_ship) - first(population_without_inst_ship))/first(population_without_inst_ship)) %>%
  mutate(pct_change_pop = percent(pct_change_pop)) %>%
  arrange(pct_change_pop) %>%
  datatable()
  


```

Output population density by ward and divison for regression modelling

Wards first

(stopped working, need to fix)

```{r}

# ward_covariates <-  glasgow_wards_1951 %>%
#   left_join(ward_pops) %>%
#   mutate(people_per_km_sq = as.double(population_without_inst_ship/area_km))
# 
# #plot it out
# 
# ward_covariates %>%
#   ggplot() +
#   geom_sf(aes(fill=people_per_km_sq)) + 
#   facet_wrap(year~., ncol=7) +
#   scale_fill_viridis_c(option="A") +
#   theme(legend.position = "bottom",
#         axis.text.x = element_text(angle = 45, hjust=1))
# 
# ggsave(here("figures/ward_pop_density.png"), width=10)
# 
# write_rds(ward_covariates, here("populations/ward_covariates.rds"))


```

Now divisions first


(stopped working, need to fix)

```{r}

# division_covariates <-  glasgow_divisions_1951 %>%
#   left_join(division_pops) %>%
#   mutate(people_per_km_sq = as.double(total_population/area_km))
# 
# #plot it out
# 
# division_covariates %>%
#   ggplot() +
#   geom_sf(aes(fill=people_per_km_sq)) + 
#   geom_sf_label(aes(label = division), size=3, fill=NA, label.size = NA, colour="black", family = "Segoe UI") +
#   facet_wrap(year~., ncol=7) +
#   scale_fill_viridis_c(option="G") +
#   theme(legend.position = "bottom",
#         axis.text.x = element_text(angle = 45, hjust=1))
# 
# ggsave(here("figures/division_pop_density.png"), width=10)
# 
# write_rds(division_covariates, here("populations/division_covariates.rds"))

```


#### 3.3 Population by age and sex

```{r}

age_sex <- read_xlsx(path = "2023-11-28_glasgow-acf.xlsx", sheet = "age_sex_population") %>%
  pivot_longer(cols = c(male, female),
               names_to = "sex")

#collapse down to smaller age groups to be manageable
age_sex <- age_sex %>%
  ungroup() %>%
  mutate(age = case_when(age == "0 to 4" ~ "00 to 04",
                         age == "5 to 9" ~ "05 to 14",
                         age == "10 to 14" ~ "05 to 14",
                         age == "15 to 19" ~ "15 to 24",
                         age == "20 to 24" ~ "15 to 24",
                         age == "25 to 29" ~ "25 to 34",
                         age == "30 to 34" ~ "25 to 34",
                         age == "35 to 39" ~ "35 to 44",
                         age == "40 to 44" ~ "35 to 44",
                         age == "45 to 49" ~ "45 to 59",
                         age == "50 to 54" ~ "45 to 59",
                         age == "55 to 59" ~ "45 to 59",
                         TRUE ~ "60 & up")) %>%
  group_by(year, age, sex) %>%
  mutate(value = sum(value)) %>%
  ungroup()



m_age_sex <- lm(value ~ splines::ns(year, knots = 3)*age*sex, data = age_sex)

summary(m_age_sex)

age_levels <- age_sex %>% select(age) %>% distinct() %>% pull() 

age_sex_nd <- 
  crossing(
    age=age_levels,
    sex=c("male", "female"),
    year = 1950:1963
  )

pred_pops <- age_sex_nd %>% modelr::add_predictions(m_age_sex)

pred_pops %>%
  ggplot(aes(x=year, y=pred, colour=age)) +
  geom_line() +
  geom_point() +
  facet_grid(sex~.) +
  scale_y_continuous(labels = comma, limits = c(0, 125000))

#How well do they match up with our overall populations?
pred_pops %>%
  group_by(year) %>%
  summarise(sum_pred_pop = sum(pred)) %>%
  right_join(overall_pops) %>%
  select(year, sum_pred_pop, population_without_inst_ship, total_population) %>%
  pivot_longer(cols = c(sum_pred_pop, population_without_inst_ship, total_population)) %>%
  ggplot(aes(x=year, y=value, colour=name)) +
  geom_point() +
  scale_y_continuous(labels = comma, limits = c(800000, 1250000))

pred_pops %>%
  group_by(year, sex) %>%
  summarise(sum = sum(pred)) %>%
  group_by(year) %>%
  mutate(sex_ratio = first(sum)/last(sum))
```





Population pyramids

```{r}

label_abs <- function(x) {
  comma(abs(x))
}


pred_pops %>%
  ungroup() %>%
  group_by(year) %>%
  mutate(year_pop = sum(pred),
         age_sex_pct = percent(pred/year_pop, accuracy=0.1)) %>%
  mutate(sex = case_when(sex=="male" ~ "Male",
                         sex=="female" ~ "Female")) %>%
  ggplot(
    aes(x = age, fill = sex, 
        y = ifelse(test = sex == "Female",yes = -pred, no = pred))) + 
  geom_bar(stat = "identity") +
  geom_text(aes(label = age_sex_pct),
            position= position_stack(vjust=0.5), colour="black", size=2.5) +
  facet_wrap(year~., ncol=7) +
  coord_flip() +
  scale_y_continuous(labels = label_abs) +
  scale_fill_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  theme_ggdist() +
  theme(axis.text.x = element_text(angle=90, hjust = 1, vjust=0.5),
        legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="", y="") 


ggsave(here("figures/s3.png"), width=10)


```

Not perfect, but resonably good. But ahhhhh... the age groups don't align with the case notification age groups! Come back to think about this later.


### 4. Tuberculosis cases

Import the tuberculosis cases dataset


#### 4.1 Overall notifications

Overall, by year.

```{r}

cases_by_year <- read_xlsx("2023-11-28_glasgow-acf.xlsx", sheet = "by_year")

cases_by_year%>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()


#shift year to midpoint
cases_by_year <- cases_by_year %>%
  mutate(year2 = year+0.5)

```

Plot the overall number of case notified per year, by pulmonary and extra pulmonary classification.

```{r}

cases_by_year %>%
  select(-total_notifications, -year) %>%
  pivot_longer(cols = c(pulmonary_notifications, `non-pulmonary_notifications`)) %>%
  mutate(name = case_when(name == "pulmonary_notifications" ~ "Pulmonary TB",
                          name == "non-pulmonary_notifications" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")
  

```

#### 4.2 Notifications by Division

Read in the datasets and merge together.

```{r}

#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")

#get the ward sheets
ward_sheets <- enframe(all_sheets) %>%
  filter(grepl("by_ward", value)) %>%
  pull(value)


cases_by_ward_sex_year <- map_df(ward_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

cases_by_ward_sex_year %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()

```

Aggregate together to get cases by division

```{r}

cases_by_division <- cases_by_ward_sex_year %>%
  left_join(ward_lookup) %>%
  group_by(division, year, tb_type) %>%
  summarise(cases = sum(cases, na.rm = TRUE))

#shift year to midpoint
cases_by_division <- cases_by_division %>%
  mutate(year2 = year+0.5) %>%
  ungroup()

cases_by_division  %>%
  select(-year2) %>%
  select(year, everything()) %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()


cases_by_division %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(division~., scales = "free_y") +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications by Division",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")

```

#### 4.3 Notifications by ward

```{r}


cases_by_ward <- cases_by_ward_sex_year %>%
  group_by(ward, year, tb_type) %>%
  summarise(cases = sum(cases, na.rm = TRUE)) %>%
  ungroup()

cases_by_ward %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  select(year, everything()) %>%
  datatable()

#shift year to midpoint
cases_by_ward <- cases_by_ward %>%
  mutate(year2 = year+0.5)

cases_by_ward %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=cases, x=year2, group = tb_type, fill=tb_type), alpha=0.8) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(ward~., scales = "free_y") +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis notifications by Ward",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Number of cases",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme(legend.position = "bottom")


```

#### 4.4 Notifications by age and sex

As we don't have denominators, we will just model the change in counts.

```{r}

#list all the sheets
all_sheets <- excel_sheets("2023-11-28_glasgow-acf.xlsx")

#get the ward sheets
age_sex_sheets <- enframe(all_sheets) %>%
  filter(grepl("by_age_sex", value)) %>%
  pull(value)


cases_by_age_sex <- map_df(age_sex_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

cases_by_age_sex %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()


```

### 4.5 Uptake of screening

What percentage of adults (15+ participated in the intervention in 1957)?

```{r}
# 
# pred_pops %>%
#   ungroup() %>%
#   filter(year==1957) %>%
#   filter(age != "00 to 04",
#          age != "05 to 14") %>%
#   summarise(total_pop = sum(pred)) %>%
#   mutate(cxr_screened = 622349) %>%
#   mutate(pct_pop_cxr_screened = percent(cxr_screened/total_pop))
# 
# pred_pops %>%
#   ungroup() %>%
#   filter(year==1957) %>%
#   filter(age != "00 to 04",
#          age != "05 to 14") %>%
#   summarise(total_pop = sum(pred), .by=sex) %>%
#   mutate(cxr_screened = c(340474, 281875)) %>%
#   mutate(pct_pop_cxr_screened = percent(cxr_screened/total_pop))


```

Note that in the Report of Sir Kenneth Cowan, we have the following estimates of participation (we will use these for the manuscript, as they are not based on my estimates)

```{r}
male_adult_resident_participation <- 281875
female_adult_resident_participation <- 340474
male_adult_resident_population <- 381713
female_adult_resident_population <- 437588

#overall participation
(male_adult_resident_participation+female_adult_resident_participation)/(male_adult_resident_population+female_adult_resident_population)

#male participation
male_adult_resident_participation/male_adult_resident_population

#female participation
female_adult_resident_participation/female_adult_resident_population


```


Look at uptake of screening by age and sex

```{r}


uptake_age_sex <- read_xlsx("2024-03-26_mass_xray_uptake.xlsx", sheet = "uptake_age_sex")

uptake_age_sex %>%
  mutate(uptake = examined/resident_population) %>%
  mutate(examined_l = comma(examined),
         resident_population_l = comma(resident_population),
         uptake_l = percent(uptake, accuracy=0.1)) %>%
  mutate(label = glue("{examined_l}/{resident_population_l} ({uptake_l})")) %>%
  filter(age !="00-14") %>%
  mutate(sex = case_when(sex=="m" ~ "Male",
                         sex=="f" ~ "Female")) %>%
  ggplot(aes(x=age, y=uptake, group=sex, fill=sex)) +
  geom_bar(stat = "identity", position = "dodge") +
  geom_text(aes(label=uptake_l), position = position_dodge(width=0.85),vjust=2) +
  scale_y_continuous(labels=percent) +
  scale_fill_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="", y="")

ggsave(here("figures/s4.png"))

```

Uptake by division

```{r}

uptake_division <- read_xlsx("2024-03-26_mass_xray_uptake.xlsx", sheet = "uptake_division")

division_pops %>%
  filter(year==1957) %>%
  select(division, population_without_inst_ship) %>%
  left_join(uptake_division) %>%
  mutate(pct_pop_examined = examined/population_without_inst_ship)


```




### 5 TB case notification rates

#### 5.1 Overall TB case notification rates

Now calculate case notification rates per 100,000 population

Merge the notification and population denominator datasets together.

Here we need to include the whole population (with shipping and institutions) as they are included in the notifications.

```{r}

overall_inc <- overall_pops %>%
  left_join(cases_by_year)

overall_inc <- overall_inc %>%
  mutate(inc_pulm_100k = pulmonary_notifications/total_population*100000,
         inc_ep_100k = `non-pulmonary_notifications`/total_population*100000,
         inc_100k = total_notifications/total_population*100000)

overall_inc %>%
  select(year, inc_100k, inc_pulm_100k, inc_ep_100k) %>%
  mutate_at(.vars = vars(inc_100k, inc_pulm_100k, inc_ep_100k),
            .funs = funs(round)) %>%
  datatable()

```

```{r}

overall_inc %>%
  select(year2, inc_pulm_100k, inc_ep_100k) %>%
  pivot_longer(cols = c(inc_pulm_100k, `inc_ep_100k`)) %>%
  mutate(name = case_when(name == "inc_pulm_100k" ~ "Pulmonary TB",
                          name == "inc_ep_100k" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=value, x=year2, group = name, fill=name), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")



```

Change in case notification rates pre-intervention

```{r}
#pre-ACF
overall_inc %>%
  filter(year %in% 1950:1956) %>%
  summarise(change = (((last(inc_pulm_100k)-first(inc_pulm_100k))/first(inc_pulm_100k))/7)*100)

#post-ACF
overall_inc %>%
  filter(year %in% 1958:1963) %>%
  summarise(change = (((last(inc_pulm_100k)-first(inc_pulm_100k))/first(inc_pulm_100k))/6)*100)

```




#### 5.2 TB case notification rates by Division

```{r}

division_inc <- division_pops %>%
  left_join(cases_by_division)


division_inc <- division_inc %>%
  mutate(inc_100k = cases/total_population*100000)

division_inc %>%
  select(year, division, tb_type, inc_100k) %>%
  mutate_at(.vars = vars(inc_100k),
            .funs = funs(round)) %>%
  datatable()


```

```{r}

division_inc %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(division~.) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate, by Division",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Case notification rate (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme_ggdist() +
  theme(legend.position = "bottom")



```

#### 5.2 TB case notification rates by Ward

Here we will filter out the institutions and harbour from the denominators, as we don't have reliable population denominators for them.

```{r}

ward_inc <- ward_pops %>%
  left_join(cases_by_ward)


ward_inc <- ward_inc %>%
  mutate(inc_100k = cases/population_without_inst_ship*100000)

ward_inc %>%
  select(year, ward, tb_type, inc_100k) %>%
  mutate_at(.vars = vars(inc_100k),
            .funs = funs(round)) %>%
  datatable()


```


```{r}

ward_inc %>%
  mutate(tb_type = case_when(tb_type == "Pulmonary" ~ "Pulmonary TB",
                          tb_type == "Non-Pulmonary" ~ "Extra-pulmonary TB")) %>%
  ggplot() +
  geom_area(aes(y=inc_100k, x=year2, group = tb_type, fill=tb_type), alpha=0.5) +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  facet_wrap(ward~.) +
  scale_fill_brewer(palette = "Set1", name="") +
  labs(
    title = "Glasgow Corporation: Tuberculosis case notification rate, by Ward",
    subtitle = "1950 to 1963, by TB disease classification",
    x = "Year",
    y = "Incidence (per 100,000)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: extra-pulmonary TB cases by Division/Ward not reported in 1962-1963"
  ) +
  theme(legend.position = "bottom")




```

On a map

```{r, warning=FALSE}

st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
  filter(tb_type=="Pulmonary") %>%
  ggplot() +
  geom_sf(aes(fill=inc_100k)) +
  facet_wrap(year~., ncol = 7) +
  scale_fill_viridis_c(name="Case notification rate (per 100,000)",
                       option = "A") +
  theme_ggdist() +
  theme(legend.position = "top",
        legend.key.width = unit(2, "cm"),
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  guides(fill=guide_colorbar(title.position = "top"))



```


### 6. TB Mortality

#### 6.1 Overall Mortality

Import the TB mortality data.

First, overall deaths. Note that in the original reports, we have a pulmonary TB death rate per million for all years, and numbers of pulmonary TB deaths for each year apart from 1950.

```{r}

#get the overall mortality sheets
deaths_sheets <- enframe(all_sheets) %>%
  filter(grepl("deaths", value)) %>%
  pull(value)


overall_deaths <- map_df(deaths_sheets, ~read_xlsx(path = "2023-11-28_glasgow-acf.xlsx",
                                sheet = .))

overall_deaths %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.))) %>%
  datatable()



```

Plot the raw numbers of pulmonary deaths

```{r}

overall_deaths %>%
  ggplot(aes(x=year, y=pulmonary_deaths)) +
  geom_line(colour = "#DE0D92") +
  geom_point(colour = "#DE0D92") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  labs(y="Pulmonary TB deaths per year",
       x = "Year",
       title = "Numbers of pulmonary TB deaths",
       subtitle = "Glasgow, 1950-1963",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)\nNote: no data for 1950") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))


```

Now the incidence of pulmonary TB death

```{r}
overall_deaths %>%
  ggplot(aes(x=year, y=pulmonary_death_rate_per_100k)) +
  geom_line(colour = "#4D6CFA") +
  geom_point(colour = "#4D6CFA") +
  geom_vline(aes(xintercept=acf_start), linetype=3) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels) +
  labs(y="Annual incidence of death (per 100,000)",
       x = "Year",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA))

ggsave(here("figures/s8.png"), width=10)

```


### 6. Table 1

Make Table 1 here, and save for publication.

```{r}

overall_pops %>% 
  select(year, total_population) %>%
  left_join(overall_inc %>%
              select(year, 
                     pulmonary_notifications, inc_pulm_100k,
                     `non-pulmonary_notifications`, inc_ep_100k,
                     total_notifications, inc_100k)) %>%
  left_join(overall_deaths %>%
              select(year,
                     pulmonary_deaths, pulmonary_death_rate_per_100k)) %>%
  mutate(percent_pulmonary = percent(pulmonary_notifications/(total_notifications ), accuracy=0.1)) %>%
  mutate(across(where(is.numeric) & !(year),  ~round(., digits=1))) %>%
  mutate(across(where(is.numeric) & !(year),  ~comma(.)))

```

Comparison fo age-sex distribution of cases in 1950-1956 vs. 1957

```{r}

label_abs2 <- function(x) {
  percent(abs(x))
}



cases_by_age_sex %>% 
  ungroup() %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(acf_period, age, sex) %>%
  summarise(cases = sum(cases)) %>%
  ungroup() %>%
  group_by(acf_period) %>%
  mutate(period_total = sum(cases)) %>%
  mutate(pct = cases/period_total) %>%
  mutate(pct2 = case_when(sex=="F" ~ -pct,
                          TRUE ~ pct)) %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
                 mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Pre-ACF",
                                               acf_period=="b. acf" ~ "ACF",
                                               acf_period=="c. post-acf" ~ "Post-ACF")) %>%
  ggplot() +
  geom_vline(aes(xintercept=0), linetype=2) +
  geom_point(aes(x=pct2,y=age, colour=fct_relevel(acf_period,
                                                             "Pre-ACF",
                                                             "ACF",
                                                             "Post-ACF")), stat="identity") +
  scale_x_continuous(labels=label_abs2, limits = c(-0.2, 0.2)) +
  scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA")) +
  theme_grey(base_family = "Aptos") +
  labs(x= "<- Female                Percent of cases              Male ->",
       y="") +
  theme(legend.title = element_blank(),
        legend.position = "bottom")

ggsave(here("figures/s5.png"))

```



Prepare the datasets for modelling

```{r}

mdata <- ward_inc %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(ward) %>%
  mutate(y_num = row_number()) %>%
  ungroup()


mdata_extrapulmonary <- ward_inc %>%
  filter(tb_type=="Non-Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  group_by(ward) %>%
  mutate(y_num = row_number()) %>%
  ungroup() %>% 
  filter(year<=1961) #no data for 1962 and 1963


#scaffold for overall predictions
overall_scaffold <- mdata %>%
    select(year, year2, y_num, acf_period, population_without_inst_ship, ward, cases) %>%
    group_by(year, year2, y_num, acf_period) %>%
    summarise(population_without_inst_ship = sum(population_without_inst_ship),
              cases = sum(cases)) %>%
    ungroup() %>%
    mutate(inc_100k = cases/population_without_inst_ship*100000) %>%
    left_join(mdata_extrapulmonary %>% group_by(year) %>%
                summarise(cases_extrapulmonary = sum(cases))) %>%
    mutate(inc_100k_extrapulmonary = cases_extrapulmonary/population_without_inst_ship*100000)

```


### 7. Pulmonary TB model

#### 7.1 Fit the model and priors

This models the case notification rate over time, with a step change for the intervention, and slope change after the intervention.

Work on the priors a bit. We will build up from less complex to more complex.

a) intercept only, to predict count of cases

at the intercept, we expect somewhere around 2500. We will set the standard deviation to both 0.5 and 1 to check what it looks like

```{r}
# 
# c(prior(lognormal(7.600902, 0.5)), #log(2500) = 7.600902
#   prior(lognormal(7.600902, 1))) %>% 
#   parse_dist() %>% 
#   
#   ggplot(aes(y = prior, dist = .dist, args = .args)) +
#   stat_halfeye(.width = c(.5, .95)) +
#   scale_y_discrete(NULL, labels = str_c("lognormal(log(2000), ", c(0.5, 1), ")"),
#                    expand = expansion(add = 0.1)) +
#   xlab(expression(exp(italic(p)(beta[0])))) +
#   coord_cartesian(xlim = c(0,15000))
# 
# 
# prior(gamma(1, 0.01)) %>%
#   parse_dist() %>%
#   ggplot(aes(y=prior, dist = .dist, args = .args)) +
#   stat_halfeye(.width = c(0.5, 0.95))
# 
# #now fit to a model, and plot some prior realisations
# 
# m_prior1 <- brm(
#   cases ~ 0 + Intercept,
#   family = negbinomial(),
#   data = overall_scaffold,
#   sample_prior = "only",
#   prior = prior(normal(log(2000), 0.5), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape)
# )
# 
# add_epred_draws(object=m_prior1,
#                 newdata = tibble(intercept=1)) %>%
#   ggplot(aes(x=intercept, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(labels = comma)


```

Now try to add in a term for the effect of y_num. We anticpate that the number of cases will decline by about 1-5% per year. However, as we are pretty uncertain about this, we will just encode a weakly regularising prior to restrict the year size to sensible ranges.

```{r}
# 
# 
# m_prior2 <- brm(
#   cases ~ 0 + Intercept + y_num,
#   family = negbinomial(),
#   data = overall_scaffold,
#   sample_prior = "only",
#   prior = prior(normal(log(2000), 0.5), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b, coef = y_num)
# )
# 
# add_epred_draws(object=m_prior2,
#                 newdata = overall_scaffold) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma)

```

Now we want to add in a prior for the effect of the acf_intervention. We anticipate the peak to be anywhere between no effect, and a tripling

```{r}
# 
# m_prior3 <- brm(
#   cases ~ 0 + Intercept + y_num + acf_period,
#   family = negbinomial(),
#   data = overall_scaffold,
#   sample_prior = "only",
#   prior = prior(normal(log(2000), 0.5), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b, coef = y_num) +
#           prior(normal(0, 0.001), class = b)
# )
# 
# 
# add_epred_draws(object=m_prior3,
#                 newdata = overall_scaffold) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(labels = comma)
# 


```

Now we look and see what it looks like with the interactions

```{r}
# 
# m_prior4 <- brm(
#   cases ~ 0 + Intercept + y_num + acf_period + y_num:acf_period,
#   family = negbinomial(),
#   data = overall_scaffold,
#   sample_prior = "only",
#   prior = prior(normal(log(2500), 1), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b)
# )
# 
# add_epred_draws(object=m_prior4,
#                 newdata = overall_scaffold) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma)
# 
# 

```

Now try adding in the random intercepts

```{r}

# c(prior(lognormal(3.912023, 0.5)), #log(50) = 3.912023
#   prior(lognormal(3.912023, 1))) %>% 
#   parse_dist() %>% 
#   
#   ggplot(aes(y = prior, dist = .dist, args = .args)) +
#   stat_halfeye(.width = c(.5, .95)) +
#   scale_y_discrete(NULL, labels = str_c("lognormal(log(50), ", c(0.5, 1), ")"),
#                    expand = expansion(add = 0.1)) +
#   xlab(expression(exp(italic(p)(beta[0])))) +
#   coord_cartesian(xlim = c(0,400))
# 
# 
# m_prior5 <- brm(
#   cases ~ y_num + acf_period + y_num:acf_period + ( 1 | ward),
#   family = negbinomial(),
#   data = mdata,
#   sample_prior = "only",
#   prior = prior(normal(log(50), 1), class = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b) +
#           prior(exponential(1), class=sd)
# )
# 
# 
# add_epred_draws(object=m_prior5,
#                 newdata = mdata,
#                 re_formula = NA) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma)
# 
# add_epred_draws(object=m_prior5,
#                 newdata = mdata,
#                 re_formula = NA) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma) +
#   facet_wrap(ward~.)

```

And add in the random slopes

```{r}
# 
# m_prior6 <- brm(
#   cases ~ 1 + y_num + acf_period + y_num:acf_period + (1 + y_num*acf_period | ward),
#   family = negbinomial(),
#   data = mdata,
#   sample_prior = "only",
#   prior = prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.1), class = b) +
#           prior(exponential(1), class=sd) +
#           prior(lkj(2), class=cor)
# )
# 
# 
# 
# m_prior6 <- brm(
#   cases ~ 0 + Intercept + y_num + acf_period + y_num:acf_period + ( y_num*acf_period | ward),
#   family = negbinomial(),
#   data = mdata,
#   sample_prior = "only",
#   prior = prior(normal(log(50), 1), class = b, coef = Intercept) +
#           prior(gamma(1, 0.01), class = shape) +
#           prior(normal(0, 0.01), class = b) +
#           prior(exponential(100), class=sd) +
#           prior(lkj(2), class=cor)
# )


# add_epred_draws(object=m_prior6,
#                 newdata = mdata,
#                 re_formula = NA) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma)
# 
# add_epred_draws(object=m_prior6,
#                 newdata = mdata,
#                 re_formula = ~( 1 + y_num + acf_period | ward)) %>%
#   ggplot(aes(x=year, y=.epred)) +
#   stat_halfeye() +
#   scale_y_log10(label=comma) +
#   facet_wrap(ward~.)
# 
# plot_counterfactual(model_data = overall_scaffold, model=m_prior6, outcome = inc_100k, 
#                     population_denominator = population_without_inst_ship, re_formula = NA)
# 
# plot_counterfactual(model_data = mdata, model=m_prior6, outcome = inc_100k, 
#                     population_denominator = population_without_inst_ship, grouping_var = ward, ward,
#                     re_formula = ~( 1 + y_num + acf_period | ward))

```


Issue here is the non-centred parameterisation of the intercept prior... Feel like this is a more interpretable way to set priors... but will revert to centred parameterisation for the meantime.


```{r}
# m_centered_prior <- brm(
#   cases ~ 1 + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)),
#                   data = mdata,
#                   family = negbinomial(),
#                   seed = 1234,
#                   chains = 4, cores = 4,
#                   prior = prior(normal(0,1000), class = Intercept) +
#                           prior(gamma(0.01, 0.01), class = shape) +
#                           prior(normal(0, 1), class = b) +
#                           prior(exponential(1), class=sd) +
#                           prior(lkj(2), class=cor),
#                   sample_prior = "only")
# 
# plot(m_centered_prior)
# 
# plot_counterfactual(model_data = overall_scaffold, model=m_centered_prior, outcome = inc_100k, 
#                     population_denominator = population_without_inst_ship, re_formula = NA)
# 
# plot_counterfactual(model_data = mdata, model=m_centered_prior, outcome = inc_100k, 
#                     population_denominator = population_without_inst_ship, grouping_var = ward, ward,
#                     re_formula = ~( 1 + y_num*acf_period | ward))

```




Look at the mean and variance of counts (counts of pulmonary notifications are what we are predicting)

```{r}

#Mean of counts per year
mean(mdata$cases)
#variance of counts per year
var(mdata$cases)

```





Quite a bit of over-dispersion here, so negative binomial distribution might be a better choice of distributional family than Poisson.

Fit the model with the data

```{r}

m_pulmonary <- brm(
  cases ~ 0 + Intercept + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)),
                  data = mdata,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4,
                  prior = prior(normal(0,1), class=b, coef = "Intercept") +
                          prior(gamma(0.01, 0.01), class = shape) +
                          prior(normal(0, 1), class = b) +
                          prior(exponential(1), class=sd) +
                          prior(lkj(4), class=cor),
  control = list(adapt_delta = 0.9))
  
#check model diagnostics
summary(m_pulmonary)
plot(m_pulmonary)

pp_check(m_pulmonary, type='ecdf_overlay')
prior_summary(m_pulmonary)

```

Nicer version of trace plots for supplemental material

```{r, fig.height=16, fig.width=16}

as_draws_df(m_pulmonary) %>% 
  bayesplot::mcmc_rank_overlay(pars = vars(b_Intercept:shape),
             facet_args = list(ncol = 4)) +
  scale_colour_scico_d(palette = "managua", name = "Chain") +
  theme_ggdist()+
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        legend.position = "top")

ggsave(here("figures/s7.png"), width=16, height=16)
```

Nicer version of table of parameters for supplement

```{r}

summarise_draws(m_pulmonary) %>%
  mutate(across(c(mean:ess_tail), comma, accuracy=0.01)) %>%
  write_csv(here("figures/s1_table.csv"))

```



#### 7.2 Summarise change in CNRs

Summarise the posterior in graphical form

```{r}

f1b <- plot_counterfactual(model_data = overall_scaffold, model = m_pulmonary, 
                           population_denominator = population_without_inst_ship, outcome = inc_100k, grouping_var=NULL,
                           re_formula = NA)
  
f1b
```

Make this into a figure combined with the map of empirical data

```{r}

f1a <- st_as_sf(left_join(ward_inc, glasgow_wards_1951)) %>%
  filter(tb_type=="Pulmonary") %>%
  ggplot() +
  geom_sf(aes(fill=inc_100k), colour="grey98", lwd=0.01) +
  facet_wrap(year~., ncol = 7) +
  scale_fill_scico(name="CNR (per 100,000)",
                       palette = "acton", direction = -1) +
  theme_grey() +
  theme(legend.position = "top",
        #legend.key.width = unit(1, "cm"),
        legend.title.align = 0.5,
        axis.text.x = element_blank(),
        axis.text.y = element_blank(),
        axis.line = element_blank(),
        axis.ticks = element_blank(),
        panel.background = element_blank(),
        legend.title = element_text(size=10))

(f1a / f1b) + plot_annotation(tag_levels = "A")

ggsave(here("figures/f1.png"), width=7, height=8)

```

Summary of change in notifications numerically

```{r}

overall_change <- summarise_change(model_data=overall_scaffold, model=m_pulmonary, 
                                   population_denominator=population_without_inst_ship, grouping_var=NULL, re_formula = NA)

#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
overall_change %>%
  keep((names(.) %in% tokeep)) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()

  
```


#### 7.3 Compared to counterfactual

Numbers of pulmonary TB cases averted compared to counterfactual per year.

```{r}

overall_pulmonary_counterf <- calculate_counterfactual(model_data = overall_scaffold, model=m_pulmonary, population_denominator = population_without_inst_ship)

overall_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()


```

Total pulmonary TB cases averted between 1958 and 1963

```{r}

overall_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()


```

#### 7.4 Correlation between RR.peak, RR.level, and RR.slope

What are the correlations between peak, level, and slope?

```{r}

#RR.peak histogram
a <- overall_change$peak_draws %>%
  ggplot() +
  geom_histogram(aes(x=estimate), fill="darkblue", colour="darkblue", alpha=0.3)+
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="RR.peak",
       y="")

#RR. level histogram
b <- overall_change$level_draws  %>%
  ggplot() +
  geom_histogram(aes(x=estimate), fill="darkblue", colour="darkblue", alpha=0.3)+
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="RR.level",
       y="")

#RR.slope histogram
c <- overall_change$slope_draws %>%
  ggplot() +
  geom_histogram(aes(x=estimate), fill="darkblue", colour="darkblue", alpha=0.3)+
  scale_fill_gradient(high="lightblue1",low="darkblue") +  
  #scale_x_continuous(limits = c(0, 6)) +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA)) +
  labs(x="RR.slope",
       y="")


#Correlation between RR.peak and RR.level
cor_rr_peak_rr_level <- round(cor(pluck(overall_change$peak_draws$estimate), pluck(overall_change$level_draws$estimate)), digits = 2)

#Correlation between RR.peak and RR.slope
cor_rr_peak_rr_slope <- round(cor(pluck(overall_change$peak_draws$estimate), pluck(overall_change$slope_draws$estimate)), digits = 2)

#Correlation between RR.level and RR.slope
cor_rr_level_rr_slope <- round(cor(pluck(overall_change$level_draws$estimate), pluck(overall_change$slope_draws$estimate)), digits = 2)


#plot of correlation between RR.peak and RR.level
d <- bind_cols(RR.peak=pluck(overall_change$peak_draws$estimate), 
          RR.level =pluck(overall_change$level_draws$estimate)) %>%
  ggplot(aes(y=RR.peak, x = RR.level)) +
  geom_hex() +
  geom_smooth(se=FALSE, colour="firebrick", method = "lm") +
  geom_text(aes(y=2.2, x=0.58, label=cor_rr_peak_rr_level), colour="firebrick")  +
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))

#plot of correlation between RR.peak and RR.slope
e <- bind_cols(RR.peak=pluck(overall_change$peak_draws$estimate), 
          RR.slope =pluck(overall_change$slope_draws$estimate)) %>%
  ggplot(aes(y=RR.peak, x = RR.slope)) +
  geom_hex() +
  geom_smooth(se=FALSE, colour="firebrick", method = "lm") +
  geom_text(aes(y=2.1, x=0.65, label=cor_rr_peak_rr_slope), colour="firebrick")  +
  #scale_x_continuous(limits = c(0, 6)) +
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))

#plot of correlation between RR.level and RR.slope
f <- bind_cols(RR.level=pluck(overall_change$level_draws$estimate), 
          RR.slope =pluck(overall_change$slope_draws$estimate)) %>%
  ggplot(aes(y=RR.level, x = RR.slope)) +
  geom_hex() +
  geom_smooth(se=FALSE, colour="firebrick", method = "lm") +
  geom_text(aes(y=0.75, x=0.65, label=cor_rr_level_rr_slope), colour="firebrick")  +  
  #scale_x_continuous(limits = c(0, 6)) +
  scale_fill_gradient(high="lightblue1",low="darkblue") +
  theme_ggdist() +
  theme(legend.position = "none",
        panel.border = element_rect(colour = "grey78", fill=NA))


(plot_spacer() + plot_spacer() + c) /
  (plot_spacer() + b + f) /
  (a + d + e)

ggsave(here("figures/s8.png"), width=8, height=8)



```


#### 7.5 Ward level pulmonary TB estimates

Plot the counterfactual at ward level

```{r}

plot_counterfactual(model_data = mdata, model=m_pulmonary, outcome = inc_100k, population_denominator = population_without_inst_ship, 
                    grouping_var = ward, ward, re_formula= ~(1 + y_num*acf_period | ward))
  
ggsave(here("figures/s6.png"), width=16, height=12)

```

Summary of change in notifications at ward level

```{r}

ward_change <- summarise_change(model_data=mdata, model=m_pulmonary, 
                                   population_denominator=population_without_inst_ship, grouping_var=ward, 
                                   re_formula = ~(1 + y_num*acf_period | ward))

#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
ward_change %>%
  keep((names(.) %in% tokeep)) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()


#plot these in a figure
ward_effects <- ward_change %>%
  keep((names(.) %in% tokeep)) %>%
  bind_rows() %>%
  bind_rows(overall_change$peak_summary) %>%
  bind_rows(overall_change$level_summary) %>%
  bind_rows(overall_change$slope_summary) %>%
  mutate_at(.vars = vars(estimate:.upper), 
            .funs = funs(as.numeric)) %>%
  select(measure, everything()) %>%
  mutate(estimate = as.double(estimate)) %>%
  full_join(glasgow_wards_1951) %>% 
  mutate(ward2 = paste0(ward_number, ". ", ward)) %>%
  mutate(ward2 = case_when(is.na(ward) ~ "Overall",
                          TRUE ~ ward2)) %>%
  st_as_sf() 

#function for plotting choropleth maps
plot_ward_effect <- function(data, measure){
  {{data}} %>%
  filter(measure == {{measure}}) %>%
  ggplot() +
  geom_sf(aes(fill=estimate)) +
  geom_sf_label(aes(label = ward_number), size=3, fill=NA, label.size = NA, colour="black") +
  scale_fill_scico(trans="log", palette = "roma", midpoint = 0, limits=c(0.5,2.25),
                       breaks = c(0.5, 0.75, 1, 1.5, 2, 2.5), labels = c(0.5, 0.75, 1, 1.5, 2, 2.5),
                   name="Relative rate") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        axis.text.x=element_text(angle=45, hjust=1)) +
    labs(x="", y="")
}

#function for plotting catapiller plots
plot_ward_cat <- function(data, measure, scale){

    ggplot() +
    geom_hline(aes(yintercept=1), linetype=2) +
    geom_pointrange(data = {{data}} %>%     
                      filter(measure=={{measure}}) %>%
                      filter(!is.na(ward)),
                    aes(y=estimate, ymin=.lower, ymax=.upper, 
                      x=fct_reorder(ward2, estimate), colour=estimate)) +
    geom_pointrange(data = {{data}} %>% 
                      filter(measure=={{measure}}) %>%
                      filter(is.na(ward)),
                    aes(y=estimate, ymin=.lower, ymax=.upper, 
                      x=ward2), colour="black") +
    coord_flip() +
    scale_colour_scico(trans="log", palette = "roma", midpoint = 0, limits=c(0.5,2.25), 
                       breaks = c(0.5, 0.75, 1, 1.5, 2, 2.5), labels = c(0.5, 0.75, 1, 1.5, 2, 2.5),
                       name="Relative rate") +
    scale_y_continuous() +
    theme_ggdist()  +
    theme(panel.border = element_rect(colour = "grey78", fill=NA)) +
    labs(x = "",
         y = "Relative rate (95% UI)")+
    guides(x = "axis_truncated", y = "axis_truncated")
}



ward_peak_i <- plot_ward_effect(data = ward_effects, measure = "RR.peak") + ggtitle("Peak effect")
ward_level_i <- plot_ward_effect(data = ward_effects, measure = "RR.level") + ggtitle("Level effect")
ward_slope_i <- plot_ward_effect(data = ward_effects, measure = "RR.slope") + ggtitle("Slope effect")

ward_peak_ii <- plot_ward_cat(data = ward_effects, measure = "RR.peak") + ggtitle("Peak effect")
ward_level_ii <- plot_ward_cat(data = ward_effects, measure = "RR.level") + ggtitle("Level effect")
ward_slope_ii <- plot_ward_cat(data = ward_effects, measure = "RR.slope") + ggtitle("Slope effect")

s4 <- (ward_peak_i + ward_level_i + ward_slope_i) /
  (ward_peak_ii + ward_level_ii + ward_slope_ii)

s4[[1]] <- s4[[1]] + plot_layout(tag_level = 'new')
s4[[2]] <- s4[[2]] + plot_layout(tag_level = 'new')
s4 + plot_annotation(tag_levels = c('A', '1')) + plot_layout(guides = 'collect') &
  theme(legend.position='bottom',
        legend.key.width = unit(3, "cm"))


ggsave(here("figures/f2.png"), width = 16, height=12)

```

Calculate the counterfactual per ward

```{r}

ward_pulmonary_counterf <- calculate_counterfactual(model_data = mdata, model=m_pulmonary, 
                                                    population_denominator = population_without_inst_ship,
                                                    grouping_var = ward, re_formula=~(1 + y_num*acf_period | ward))

ward_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()


```

Overall counterfactual per ward

```{r}

ward_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()

```



### 8. Extra-pulmonary TB notifications

Now we will model the extra-pulmonary TB notification rate. Struggling a bit with negative binomial model, so revert to Poisson.

#### 8.1 Fit the model

```{r}

m_extrapulmonary <- brm(
  cases ~ 1 + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)),
                  data = mdata_extrapulmonary,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4,
                  prior = prior(normal(0,1000), class = Intercept) +
                          prior(gamma(0.01, 0.01), class = shape) +
                          prior(normal(0, 1), class = b) +
                          prior(exponential(1), class=sd) +
                          prior(lkj(2), class=cor))

summary(m_extrapulmonary)
plot(m_extrapulmonary)
pp_check(m_extrapulmonary, type='ecdf_overlay')

```

#### 8.2 Summary of change

Summarise in plot

```{r}
plot_counterfactual(model_data = overall_scaffold %>% filter(year<=1961), model=m_extrapulmonary, 
                    population_denominator = population_without_inst_ship, outcome=inc_100k_extrapulmonary, re_formula = NA) +
  scale_y_continuous(limits = c(0,50))
  
ggsave(here("figures/s9.png"), width=10)

```

Summarise numerically.

```{r}

overall_change_extrapulmonary <- summarise_change(model_data=overall_scaffold, model=m_extrapulmonary, 
                                   population_denominator=population_without_inst_ship, grouping_var=NULL, re_formula = NA)

#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
overall_change_extrapulmonary %>%
  keep(names(.) %in% tokeep) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()

```

#### 8.3 Compared to counterfactual

Numbers of extra-pulmonary TB cases averted overall.

```{r}

overall_ep_counterf <- calculate_counterfactual(model_data = mdata_extrapulmonary, model=m_extrapulmonary, 
                                               population_denominator = population_without_inst_ship)

overall_ep_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()

```

Total extrapulmonary TB cases averted between 1958 and 1963

```{r}

overall_ep_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  datatable()


```

Make into Table 2


```{r}
bind_rows(
overall_pulmonary_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "PTB_ward"),

overall_pulmonary_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "PTB_overall"),

overall_ep_counterf$counter_post %>%
  mutate(across(c(cases_averted:cases_averted.upper, diff_inc100k:diff_inc100k.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(rr_inc100k:rr_inc100k.upper), number_format(accuracy = 0.01))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "EPTB"),

overall_ep_counterf$counter_post_overall %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  mutate(model = "EPTB overall")

) %>%
  select(model, year, diff_inc100k, diff_inc100k.lower:rr_inc100k.upper, 
         cases_averted:cases_averted.upper,
         pct_change:pct_change.upper) %>%
  transmute(model=model, year=year,
            diff_cnr = glue("{diff_inc100k} ({diff_inc100k.lower} to {diff_inc100k.upper})"),
            rr = glue("{rr_inc100k} ({rr_inc100k.lower} to {rr_inc100k.upper})"),
            cases_averted = glue("{cases_averted} ({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue("{pct_change} ({pct_change.lower} to {pct_change.upper})")) %>%
  write_csv(here("figures/table2.csv"))



```




#### 8.4 Ward-level extra-pulmonary summaries

Ward-level extra-pulmonary estimates in graphical form.

```{r}

plot_counterfactual(model_data = mdata_extrapulmonary, model=m_extrapulmonary, outcome = inc_100k, 
                    population_denominator = population_without_inst_ship, grouping_var = ward,re_formula =~(y_num*acf_period | ward), 
                    ward) + scale_y_continuous(limits= c(0,75))
  
ggsave(here("figures/s10.png"), width=10, height=12)

```

Numerical summary.

```{r}

ward_change_extrapulmonary <- summarise_change(model_data = mdata_extrapulmonary, model = m_extrapulmonary, 
                                population_denominator = population_without_inst_ship, grouping_var=ward,
                                re_formula = ~(y_num*acf_period | ward)) 

#want to keep the summary estimates here
tokeep <- c("peak_summary", "level_summary", "slope_summary")

#summary measures in a table
ward_change_extrapulmonary  %>%
  keep(names(.) %in% tokeep) %>%
  bind_rows() %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()



```


### 9. Age-sex model

#### 9.1 FIt the model

Fit the model

(Not rewritten the functions for this yet)

```{r}

mdata_age_sex <- cases_by_age_sex %>%
  filter(tb_type=="Pulmonary") %>%
  mutate(acf_period = case_when(year %in% c(1950:1956) ~ "a. pre-acf",
                                year %in% c(1957) ~ "b. acf",
                                year %in% c(1958:1963) ~ "c. post-acf")) %>%
  mutate(year2 = year+0.5) %>%
  group_by(age, sex) %>%
  mutate(y_num = row_number()) %>%
  ungroup()

m_age_sex <- brm(
  cases ~ y_num + (acf_period)*(age*sex) + (acf_period:y_num)*(age*sex),
                  data = mdata_age_sex,
                  family = negbinomial(),
                  seed = 1234,
                  chains = 4, cores = 4, 
                  prior = prior(normal(0,1), class = Intercept) +
                          prior(gamma(0.01, 0.01), class = shape) +
                          prior(normal(0, 1), class = b))

summary(m_age_sex)
plot(m_age_sex)
pp_check(m_age_sex, type='ecdf_overlay')

```

Summarise posterior


```{r}

#posterior draws, and summarise
age_sex_summary <- mdata_age_sex %>%
  select(year, year2, y_num, acf_period, age, sex) %>%
  add_epred_draws(m_age_sex) %>%
  group_by(year2, acf_period, age, sex) %>%
  mean_qi() %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention"))

#create the counterfactual (no intervention), and summarise
age_sex_counterfact <- 
  tibble(year = mdata_age_sex$year,
         year2 = mdata_age_sex$year2,
         y_num = mdata_age_sex$y_num,
         age = mdata_age_sex$age,
         sex = mdata_age_sex$sex,
         acf_period = factor("a. pre-acf")) %>%
  add_epred_draws(m_age_sex) %>%
  group_by(year2, acf_period, age, sex) %>%
  mean_qi() %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="c. post-acf" ~ "Post Intervention")) %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) 



age_sex_summary %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) %>%
  ggplot() +
  geom_ribbon(aes(ymin=.epred.lower, ymax=.epred.upper, x=year2, group = acf_period, fill=acf_period), alpha=0.5) +
  geom_ribbon(data = age_sex_counterfact %>% filter(year>=1956), 
              aes(ymin=.epred.lower, ymax=.epred.upper, x=year2, fill="Counterfactual"), alpha=0.5) +
  geom_line(data = age_sex_counterfact %>% filter(year>=1956), 
              aes(y=.epred, x=year2, colour="Counterfactual")) +
  geom_line(aes(y=.epred, x=year2, group=acf_period,  colour=acf_period)) +
  geom_point(data = mdata_age_sex %>%
  ungroup() %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  mutate(sex = case_when(sex== "M" ~ "Male",
                         sex== "F" ~ "Female")) %>%
  mutate(acf_period = case_when(acf_period=="a. pre-acf" ~ "Before Intervention",
                                acf_period=="b. acf" ~ "Counterfactual",
                                  acf_period=="c. post-acf" ~ "Post Intervention"))%>%
                 mutate(acf_period2 = case_when(acf_period=="Before Intervention" ~ "Pre-ACF",
                                               acf_period=="Counterfactual" ~ "ACF",
                                               acf_period=="Post Intervention" ~ "Post-ACF")), 
  aes(y=cases, x=year2, shape=fct_relevel(acf_period2,
                                                             "Pre-ACF",
                                                             "ACF",
                                                             "Post-ACF")), size=2) +
  geom_vline(aes(xintercept=acf_end), linetype=3) +
  ggh4x::facet_grid2(age~sex, scales = "free_y", independent = "y") +
  theme_ggdist() +
  scale_y_continuous(labels=comma) +
  scale_x_continuous(labels = year_labels,
                     breaks = year_labels,
                     guide = guide_axis(angle = 90)) +
  scale_fill_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:") +
  scale_colour_manual(values = c("#DE0D92", "grey50", "#4D6CFA") , name="Model estimates:") +
  scale_shape_discrete(name="Emprical data (period):", na.translate = F) +
  labs(
    x = "Year",
    y = "Case notifications (n)",
    caption = "Mass miniature X-ray campaign period between dashed lines (11th March-12th April 1957)"
  ) +
  theme(legend.position = "bottom",          
        legend.box="vertical", 
        panel.border = element_rect(colour = "grey78", fill=NA))
  
ggsave(here("figures/s12.png"), height=10)

```

#### 9.2 Summary of impact of intervention

Calculate summary effects

```{r}

#Peak
out_age_sex_1 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num == 8),
                      acf_period = c("a. pre-acf", "b. acf"))

peak_draws_age_sex <- add_epred_draws(newdata = out_age_sex_1,
                  object = m_age_sex) %>%
    group_by(.draw, age, sex) %>%
    summarise(estimate = last(.epred)/first(.epred)) %>%
    ungroup() %>%
    mutate(measure = "RR.peak")
  
peak_summary_age_sex <- peak_draws_age_sex %>%
    group_by(age, sex) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.peak")


#Level
 
out_age_sex_2 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num == 9),
                      acf_period = c("a. pre-acf", "c. post-acf"))
  
level_draws_age_sex <- add_epred_draws(newdata = out_age_sex_2,
                  object = m_age_sex) %>%
    arrange(y_num, .draw) %>%
    group_by(.draw, age, sex) %>%
    summarise(estimate = last(.epred)/first(.epred)) %>%
    ungroup() %>%
    mutate(measure = "RR.level")
  
level_summary_age_sex <- level_draws_age_sex %>%
    group_by(age, sex) %>%
    mean_qi(estimate) %>%
    mutate(measure = "RR.level")

#Slope

out_age_sex_3 <- crossing(mdata_age_sex %>% 
                      select(y_num, age, sex) %>%
                      filter(y_num %in% c(9,14)),
                    acf_period = c("a. pre-acf", "c. post-acf"))
  
slope_draws_age_sex <- add_epred_draws(newdata = out_age_sex_3,
                  object = m_age_sex) %>%
        arrange(y_num) %>%
        ungroup() %>%
        group_by(.draw, y_num, age, sex) %>%
        summarise(slope = last(.epred)/first(.epred)) %>%
        ungroup() %>%
        group_by(.draw, age, sex) %>%
        summarise(estimate = last(slope)/first(slope)) %>%
        mutate(measure = "RR.slope")
  
slope_summary_age_sex <- slope_draws_age_sex %>%
     group_by(age, sex) %>%
      median_qi(estimate) %>%
      mutate(measure = "RR.slope")

```


Numerical summary of these summary results

```{r}

bind_rows(
  peak_summary_age_sex, level_summary_age_sex, slope_summary_age_sex
) %>%
  mutate(across(c(estimate:.upper), number, accuracy=0.01)) %>%
  select(measure, everything()) %>%
  datatable()



```

As a figure

```{r}

peak_g_age_sex <- peak_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

#level plot
level_g_age_sex <- level_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))

#slope plot
slope_g_age_sex <- slope_summary_age_sex %>%
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_hline(aes(yintercept=1), linetype=2)+
  geom_pointrange(aes(x=age, y=estimate, ymin=.lower, ymax=.upper, group=sex, colour=sex, shape=sex),
                  position = position_dodge(width = 0.5)) +
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  labs(x="",
       y="Relative rate (95% UI)") +
  theme_ggdist() +
  theme(legend.position = "bottom",
        panel.border = element_rect(colour = "grey78", fill=NA))


```


#### 9.3 Compared to counterfactual

```{r}

counterfact_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex) %>%
                                    mutate(acf_period = "a. pre-acf")) %>%
      filter(year>1957) %>%
      select(year, age, sex, .draw, .epred_counterf = .epred)
  
#Calcuate predicted number of cases per draw, then summarise.
post_change_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex, acf_period)) %>%
      filter(year>1957) %>%
      ungroup() %>%
      select(year, age, sex, .draw, .epred) 
  
#for the overall period
counterfact_overall_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex) %>%
                                    mutate(acf_period = "a. pre-acf")) %>%
      filter(year>1957) %>%
      select(age, sex, .draw, .epred)  %>%
      group_by(age, sex, .draw) %>%
      summarise(.epred_counterf = sum(.epred)) %>%
      mutate(year = "Overall (1958-1963)")
  
#Calcuate incidence per draw, then summarise.
post_change_overall_age_sex <-
      add_epred_draws(object = m_age_sex,
                      newdata = mdata_age_sex %>%
                                    select(year, year2, y_num, age, sex, acf_period)) %>%
      filter(year>1957) %>%
      select(age, sex, .draw, .epred) %>%
      group_by(.draw, age, sex) %>%
      summarise(.epred = sum(.epred)) 
  
counter_post_overall_age_sex <-
  left_join(counterfact_overall_age_sex, post_change_overall_age_sex) %>%
    mutate(cases_averted = .epred_counterf-.epred,
           pct_change = (.epred - .epred_counterf)/.epred_counterf) %>%
    group_by(age, sex) %>%
    mean_qi(cases_averted, pct_change) %>%
    ungroup() %>%
    mutate(year = "Overall (1958-1963)") 


age_sex_txt <- counter_post_overall_age_sex %>%
  mutate(across(c(cases_averted:cases_averted.upper), number_format(accuracy = 0.1, big.mark = ","))) %>%
  mutate(across(c(pct_change:pct_change.upper), percent, accuracy=0.1)) %>%
  transmute(year = as.character(year),
            sex = sex,
            age = age,
            cases_averted = glue::glue("{cases_averted}\n({cases_averted.lower} to {cases_averted.upper})"),
            pct_change = glue::glue("{pct_change}\n({pct_change.lower} to {pct_change.upper})"))


age_sex_txt %>% datatable()


```

```{r}

counterfactual_g_age_sex <- counter_post_overall_age_sex %>% 
  mutate(sex = case_when(sex=="M" ~ "Male",
                         sex=="F" ~ "Female")) %>%
  mutate(age = case_when(age=="00_05" ~ "0 to 5y",
                         age=="06_15" ~ "06 to 15y",
                         age=="16_25" ~ "16 to 25y",
                         age=="26_35" ~ "26 to 35y",
                         age=="36_45" ~ "36 to 45y",
                         age=="46_55" ~ "46 to 55y",
                         age=="56_65" ~ "56 to 65y",
                         age=="65+" ~ "65 & up y")) %>%
  ggplot() +
  geom_pointrange(aes(x = age, y=cases_averted, ymin=cases_averted.lower, ymax=cases_averted.upper, colour=sex, shape=sex), position=position_dodge(width=0.5)) + 
  scale_colour_manual(values = c("#CD7AC5", "cadetblue3"), name="") +
  scale_shape(name="") +
  scale_y_continuous(labels = comma) +
  labs(x="",
       y="Number (95% UI)",
       colour="") +
  theme_ggdist() +
  theme(panel.border = element_rect(colour = "grey78", fill=NA),
        legend.position = "bottom")

counterfactual_g_age_sex
```

Join together for Figure 3.


```{r}

(peak_g_age_sex + level_g_age_sex) / (slope_g_age_sex + counterfactual_g_age_sex) + plot_annotation(tag_levels = "A") + plot_layout(guides = "collect") & theme(legend.position = "bottom")

ggsave(here("figures/f3.png"), width = 12, height=8)


```



### 10 Division model

Was uptake of CXR at division level associated with greated impact?

# ```{r}
# 
# m_division <- brm(
#   cases ~ 1 + y_num*acf_period + (1 + y_num*acf_period | ward) + offset(log(population_without_inst_ship)),
#                   data = mdata,
#                   family = negbinomial(),
#                   seed = 1234,
#                   chains = 4, cores = 4,
#                   prior = prior(normal(0,1), class = Intercept) +
#                           prior(gamma(0.01, 0.01), class = shape) +
#                           prior(normal(0, 1), class = b) +
#                           prior(exponential(1), class=sd) +
#                           prior(lkj(4), class=cor),
#   control = list(adapt_delta = 0.9))
# 
# ```

